System, method, and program for augmenting training data used for machine learning

ABSTRACT

The problem to be solved is to provide a system and the like for augmenting supervisory data while maintaining the relationship among a plurality supervisory data used for machine learning. The present disclosure provides a system for augmenting supervisory data used for machine learning, the system including an obtaining means that obtains a plurality of supervisory data, a first processing means that derives a covariance matrix from the plurality of supervisory data, a second processing means that decomposes the covariance matrix, and a third processing means that applies a random number to the decomposed matrix.

TECHNICAL FIELD

The present disclosure relates to a system, method and program foraugmenting supervisory data used for machine learning. Morespecifically, the present disclosure relates to a system and the likefor augmenting reaction data obtained from an organism upon analyzingthe organism as an object. Even more specifically, the presentdisclosure relates to a system and the like for augmenting brainwavedata or analysis data thereof obtained from an object being estimatedupon generating a pain classifier for classifying the pain the objectbeing estimated has based on the brainwave of the object beingestimated. The present disclosure is also related to a system and thelike for augmenting brainwave data or analysis data thereof obtainedfrom an object upon generating a model for differentiating the pain ofthe object.

BACKGROUND ART

Pain is intrinsically subjective, but objective evaluation thereof isdesirable for therapy. Patients often suffer from an undesirableexperience due to underestimation of pain. In this regard, a method forobjectively estimating pain using brainwaves has been proposed (see, forexample, Patent Literature 1).

However, the intensity of pain is subjective, so that objectiveevaluation is challenging. Brainwave signals also vary widely such thatthe signals do not necessarily correspond to subjective evaluation.Further, a methodology for effectively monitoring temporal changes inpain has not been established. Pain differentiation is still at anascent stage, such that efficient model generation and differentiationmethods have not been provided.

Machine learning is widely used as a method for materializing artificialintelligence. Many supervisory data are required to be learned in orderto improve accuracy of machine learning.

CITATION LIST Patent Literature

-   [PTL 1] Japanese National Phase PCT Laid-open Publication No.    2010-523226

SUMMARY OF INVENTION Solution to Problem

In one aspect, the present disclosure provides a system, method andprogram for augmenting supervisory data used for machine learning. Morespecifically, the present disclosure provides a system and the like foraugmenting reaction data obtained from an organism upon analyzing theorganism as an object. Furthermore, the present disclosure provides asystem and the like for augmenting brainwave data or analysis datathereof obtained from an object being estimated upon generating a painclassifier for classifying the pain the object being estimated has basedon the brainwave of the object being estimated. The present disclosureis also related to a system and the like for augmenting brainwave dataor analysis data thereof obtained from an object upon generating a modelfor differentiating the pain of the object.

In this aspect, the present disclosure provides, for example, thefollowing items.

(Item 1)

A system for augmenting supervisory data used for machine learning,comprising:

an obtaining means obtaining a plurality of supervisory data;

a first processing means deriving a covariance matrix from the pluralityof supervisory data;

a second processing means decomposing the covariance matrix; and

a third processing means applying a random number to the decomposedmatrix.

(Item 2)

The system of item 1, wherein the supervisory data is data obtained froman organism.

(Item 3)

The system of item 2, wherein the supervisory data is brainwave data,MRI image data, or gene expression data.

(Item 4)

The system of item 3, wherein the supervisory data is brainwave data orMRI data of when pain is applied to the organism.

(Item 5)

The system of any one of items 1 to 4, wherein: the second processingmeans is configured to decompose the covariance matrix into Q*Q′,wherein matrix Q′ is a transposed matrix of matrix Q; and

the third processing means is configured to apply a random number to thematrix Q or the matrix Q′.

(Item 6)

The system of item 5, wherein: the second processing means is configuredto decompose the covariance matrix into Q*Q′ by carrying out one ofCholesky decomposition, LU decomposition and QR decomposition to thecovariance matrix, wherein the matrix Q or matrix Q′ is an uppertriangular matrix; and

the third processing means is configured to apply a random number to theupper triangular matrix.

(Item 7)

The system of any one of items 1 to 6, wherein: the first processingmeans is further configured to calculate a mean value vector of theplurality of supervisory data; and

the third processing means is further configured to add a mean valuevector to the decomposed matrix to which the random number has beenapplied.

(Item 8)

The system of any one of items 1 to 4, wherein: the first processingmeans is configured to

-   -   calculate a mean value of the plurality of supervisory data,    -   subtract a mean value from the plurality of supervisory data,        and    -   derive a covariance matrix from a plurality of supervisory data        in which the mean value has been subtracted;

the second processing means is configured to

-   -   decompose the covariance matrix into V*L*V′, wherein matrix L is        a diagonal matrix consisting of an eigenvalue, matrix V is a        matrix having a corresponding right eigenvector in a column, and        matrix V′ is a transposed matrix of matrix V, wherein when sqrt(        ) is set as a function employing a square root, matrix M=sqrt(L)        is expressed, and

the third processing means is configured to

-   -   apply a random number to the matrix M,

wherein the system further comprises a fourth processing means carryingout projection conversion of the matrix M to which the random number hasbeen applied, wherein the fourth processing means adds the mean value tomatrix M that underwent projection conversion.

(Item 9)

The system of any one of items 1 to 8, further comprising a dividingmeans dividing the plurality of supervisory data into a plurality ofsubsets,

wherein the first processing means, the second processing means and thethird processing means carry out each processing to each of theplurality of subsets.

(Item 10)

A pain estimation system estimating pain that an object being measuredhas, comprising:

the system for augmenting supervisory data used for machine learning ofitem 1; and

a system for learning a plurality of supervisory data augmented by thesystem for augmenting the supervisory data and generating a painestimation model.

(Item 11)

A method for augmenting supervisory data used for machine learning,comprising:

obtaining a plurality of supervisory data;

deriving a covariance matrix from the plurality of supervisory data;

decomposing the covariance matrix; and

applying a random number to the decomposed matrix.

(Item 12)

A program for augmenting supervisory data used for machine learning,wherein the program is performed in a computer system comprising aprocessor, wherein the program causes the processor unit to perform aprocessing comprising:

obtaining a plurality of supervisory data;

deriving a covariance matrix from the plurality of supervisory data;

decomposing the covariance matrix; and

applying a random number to the decomposed matrix.

(Item 13)

A method of generating a pain classifier for classifying pain that anobject being estimated has based on a brainwave of the object beingestimated, comprising:

a) the step of stimulating the object being estimated with a pluralityof levels of stimulation intensities;

b) the step of obtaining brainwave data or analysis data thereof of theobject being estimated corresponding to the stimulation intensity;

c) the step of augmenting brainwave data or analysis data thereof of theobject being estimated, comprising:

-   -   i) deriving a covariance matrix from brainwave data or analysis        data thereof of the object being estimated;    -   ii) decomposing the covariance matrix; and    -   iii) applying a random number to the decomposed matrix;

d) the step of plotting the stimulation intensity or a subjective painsensation level corresponding to the stimulation intensity and theaugmented brainwave data or analysis data thereof to fit to a painfunction to obtain a pain function specific to the object beingestimated; and

e) the step of, when regression coefficient of the fitting to thespecific pain function is equal to or more than what is predetermined,identifying a pain classifier for dividing a pain level into at leasttwo or more based on the specific pain function.

(Item 14)

An apparatus generating a pain classifier for classifying pain that anobject being estimated has based on a brainwave of the object beingestimated, comprising:

A) a stimulation unit stimulating the object being estimated with aplurality of levels of stimulation intensities;

B) a brainwave data obtaining unit obtaining brainwave data or analysisdata thereof the object being estimated corresponding to the stimulationintensity;

C) an augmentation unit augmenting brainwave data or analysis datathereof of the object being estimated, wherein the augmentation unit isconfigured to:

-   -   i) derive a covariance matrix from brainwave data or analysis        data thereof of the object being estimated;    -   ii) decompose the covariance matrix; and    -   iii) apply a random number to the decomposed matrix; and

D) a pain classifier generation unit plotting the stimulation intensityor a subjective pain sensation level corresponding to the stimulationintensity and the augmented brainwave data or analysis data thereof tofit to a pain function to obtain a pain function specific to the objectbeing estimated and identifying a pain classifier for dividing a painlevel into at least two or more based on the specific pain function.

(Item 15)

A method of generating a model for differentiating pain of an object,comprising:

a) the step of obtaining brainwave data or analysis data thereof fromthe object;

b) the step of contracting features based on the brainwave data oranalysis data thereof with respect to the pain after determining afeature coefficient associated with the pain;

c) augmenting the features that have been weighted after the contractingor combination thereof, comprising:

-   -   i) deriving a covariance matrix from the features that have been        weighted after the contracting or combination thereof;    -   ii) decomposing the covariance matrix; and    -   iii) applying a random number to the decomposed matrix;

d) the step of creating a differentiation analysis model by machinelearning and examination based on the augmented features or combinationthereof; and

d) the step of determining a differentiation analysis model achieving apredetermined precision.

(Item 16)

A system generating a model for differentiating pain of an object, thesystem comprising:

A) a brainwave data obtaining unit obtaining brainwave data or analysisdata thereof from the object;

B) a feature contracting unit contracting features based on thebrainwave data or analysis data thereof with respect to the pain afterdetermining a feature coefficient associated with the pain;

C) an augmentation unit augmenting the features that have been weightedafter the contracting or combination thereof, wherein the augmentationunit is configured to:

-   -   i) derive a covariance matrix from the features that have been        weighted after the contracting or combination thereof;    -   ii) decompose the covariance matrix;    -   iii) apply a random number to the decomposed matrix; and

D) a pain differentiation/estimation model generation unit creating adifferentiation analysis model by machine learning and examination basedon the augmented features that have been weighted after the contractingor combination thereof.

(Item 17)

A method of analyzing an organism as an object, comprising:

a) the step of stimulating the organism with a plurality of types ofstimulations;

b) the step of obtaining reaction data of the organism corresponding tothe stimulation type;

c) augmenting reaction data of the organism, comprising:

-   -   i) deriving a covariance matrix from reaction data of the        organism;    -   ii) decomposition the covariance matrix; and    -   iii) applying a random number to the decomposed matrix; and

d) the step of plotting the stimulation type and the augmented reactiondata for analysis.

(Item 18)

The method of item 17, wherein stimulation of the organism isstimulation to gene or candidate of an agent, and the reaction datacomprises gene expression data or reaction of an organism.

(Item 19)

A method of generating a model for differentiating pain of an object,comprising:

a) the step of obtaining a plurality of COVAS data by carrying out apain test to a plurality of subjects;

b) the step of creating a COVAS template by averaging the plurality ofCOVAS data;

c) the step of obtaining brainwave data or analysis data thereof fromthe object by carrying out the pain test to the object;

d) the step of cutting out the brainwave data or analysis data thereofbased on the COVAS template; and

e) the step of creating a model by setting the cut out brainwave data oranalysis data thereof as data for learning and learning a value of aCOVAS template corresponding to the cut out brainwave data or analysisdata thereof as a label.

(Item 20)

The method of item 19, wherein the learning comprises the step ofaugmenting the cut out brainwave data or analysis data thereof,comprising:

i) deriving a covariance matrix from the cut out brainwave data oranalysis data thereof;

ii) decomposing the covariance matrix; and

iii) applying a random number to the decomposed matrix.

The present disclosure is intended so that one or more of theaforementioned characteristics can be provided not only as theexplicitly disclosed combinations, but also as other combinationsthereof. Additional embodiments and advantages of the present disclosureare recognized by those skilled in the art by reading and understandingthe following detailed description as needed.

Advantageous Effects of Invention

The present disclosure can augment supervisory data while maintainingthe relationship among a plurality of supervisory data used for machinelearning. The use of such augmented supervisory data for machinelearning does not compromise prediction accuracy and can achieve theintended prediction accuracy. This is because, for example, a highlyaccurate and highly reliable prediction by machine learning can becarried out even when there is only a small number of samples. This isespecially useful when learning data obtained from an organism anduseful when learning reaction data against stimulation.

The present disclosure can also efficiently differentiate pain from asmall number of samples. Pain can be differentiated at an exceptionallyhigh level of accuracy, which enables therapy or surgery that is furtherdetailed and matching with the subjectivity, which is useful in themedical-related industry.

The present disclosure can provide a system and the like for augmentingsupervisory data while maintaining the relationship among a plurality ofsupervisory data used for machine learning as described above.

Accordingly, when learning data obtained from an organism, the sampleaugmentation of the present disclosure can reduce the burden imposed onan organism as much as possible instead of imposing many stimulations onthe organism in order to obtain many supervisory data when, for example,obtaining reaction data against the stimulation, in a scene where it isdifficult to obtain many supervisory data or the like, such as when thenumber of samples is generally limited.

While simple augmentation of the number of a plurality of supervisorydata is insufficient as supervisory data for machine learning and cannotachieve the intended prediction accuracy and high prediction accuracy isrequired when, for example, learning data obtained from an organism, thepresent disclosure can also improve the low reliability that can be seenin the prediction by learning the supervisory data in which the numberof a plurality of supervisory data has been simply augmented.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows an example of a configuration of a system 100 foraugmenting supervisory data used for machine learning.

FIG. 2 shows another example of a system 100 for augmenting supervisorydata used for machine learning.

FIG. 3 is a flow chart showing an example of a processing 300 in asystem 100 for augmenting supervisory data used for machine learning.

FIG. 4 is a flow chart showing an example of a processing 400 in asystem 100 for augmenting supervisory data used for machine learning.

FIG. 5A is a flow chart showing an example of a processing 500 in asystem 100 for augmenting supervisory data used for machine learning.

FIG. 5B A flow chart of a processing equivalent to the processing 500.

FIG. 6A is a graph showing the relationship between electricalstimulation and pain levels (VAS).

FIG. 6B is a graph showing the relationship between electricalstimulation and pain levels (paired comparison).

FIG. 6C is a graph showing the relationship between electricalstimulation and brainwave amplitude.

FIG. 6D is a graph showing an example of a waveform of a brainwave.

FIG. 6E is a graph showing the relationship between pain level due toelectrical stimulation (VAS) and brainwave amplitude.

FIG. 6F is a graph showing the relationship between pain level due toelectrical stimulation (paired comparison) and brainwave amplitude.

FIG. 6G is a graph showing the relationship between pain level due tohot stimulation (VAS) and brainwave amplitude.

FIG. 6H is a graph showing the relationship between pain level due tohot stimulation (paired comparison) and brainwave amplitude.

FIG. 7 shows the change in the correlation coefficient when changing thearithmetic mean time interval from 10 seconds to 120 seconds using hightemperature stimulation test data (six levels of pain). A tendency for acorrelation coefficient to increase with longer mean time interval isobserved. A shows the correlation between arithmetic mean potentialamplitude (absolute value) and subjective pain evaluation, and B showscorrelation between arithmetic mean potential amplitude (absolute value)and high temperature stimulation intensity. The vertical axis indicatesthe correlation coefficient, and the horizontal axis indicates thearithmetic mean time interval (seconds).

FIG. 8 shows the change in the correlation coefficient when changing thegeometric mean time interval from 10 seconds to 120 seconds using hightemperature stimulation test data (six levels of pain). A tendency for acorrelation coefficient to increase with longer mean time interval isobserved. A shows the correlation between geometric mean potentialamplitude (absolute value) and subjective pain evaluation, and B showscorrelation between geometric mean potential amplitude (absolute value)and high temperature stimulation intensity. The vertical axis indicatesthe correlation coefficient, and the horizontal axis indicates thegeometric mean time interval (seconds).

FIG. 9 is a diagram of a scheme of the present disclosure.

FIG. 10 is a block diagram of the configuration of the apparatus of thepresent disclosure.

FIG. 11 shows an outline of the analysis method of Example 2 in Example7. The property of temporal change in the frequency feature (P band)exhibiting the property of delayed pain sensation of C fiber isillustrated (displayed by time frequency analysis). In particular, thefrequency power is increased compared to other levels with a delay of 5seconds after applying stimulation of level 6.

FIG. 12 shows a differentiation model (standardized in rest section,with EOG feature). Subjects at or below the chance level were removed,and the model was recreated 4 times (81 subjects, 4860 samples). Thebase model was created by differentiation of L1 (40° C.) and L6 (50°C.). The left side shows feature coefficients (coefficients of defaultpain differentiation model) after refinement. The right graph shows theoverall distribution of pain estimation values after four refinements.

FIG. 13 shows pain occupancy. This is a concept indicating the certaintywhen a pain estimation value is differentiated as “having pain”. Thecalculation method thereof is the following. i. Calculate an estimatefor no pain (30 samples×81 subjects) and having pain (30 samples×81subjects) by a differentiation model for 81 subjects (differentiationaccuracy >70%) after refinement. ii. The minimum value (−1) and maximumvalue (2) of all pain estimation values are divided into, for example,0.1 widths, and the occupancy of estimation value for having pain amongeach estimation value is calculated: “occupancy (%) for havingpain=number of estimation value for having pain/(number of estimationvalues for having pain+number of estimation values for no pain)”. iii.This is fitted to a sigmoid function to obtain a “pain occupancyfunction” to create an occupancy model. An example of a pain occupancyfunction is shown below.

FIG. 14 shows the flow of differentiation analysis with a process forcontracting the number of features.

FIG. 15 shows a schematic diagram of a differentiation apparatus with aprocess for contracting features.

FIG. 16 is a schematic diagram of the device of the present disclosure.This presumes an example equipped with all parts.

FIG. 17 is a schematic diagram of the device of the present disclosure,showing an embodiment of the device portion (left side) having only afunction for obtaining brainwaves, transmitting/receiving data, andmaking them visible. This is an embodiment presuming that analysis anddetermination/differentiation model generation and the like areperformed on the cloud or a server. A brainwave feature (analysis data)is extracted on the terminal side in this model.

FIG. 18 is a schematic diagram of the device of the present disclosure,showing an embodiment of the device portion (left side) having only afunction for obtaining brainwaves, transmitting/receiving data, andmaking them visible. This is an embodiment presuming that analysis anddetermination/differentiation model generation and the like areperformed on the cloud or a server. A brainwave feature (analysis data)is extracted on the server side in this model.

FIG. 19 is a schematic diagram of the device of the present disclosure,showing an embodiment of the device portion (left side) having afunction for obtaining brainwaves, transmitting/receiving data, enablingon-site differentiation by storing a differentiation model, and makingthem visible. Such an embodiment presumes use at a facility or locationwhere it is difficult to transmit/receive radio waves such as ahospital. This is an embodiment where a determination/differentiationmodel is generated on the cloud or server, and actual measurement datais actually fitted to a model at a device. The brainwave feature(analysis data) can be extracted at a terminal or on the server side.

FIG. 20 shows an example of the flow of a determination process indifferentiation analysis with a process of contracting the number offeatures. This is an example for 1) quantifying the featuredifferentiation properties, 2) ranking features, 3) performinghierarchical differentiation analysis with features included in orderfrom the top feature, and 4) determining a differentiation model. Allnumerical values in the figure are exemplary.

FIG. 21 shows a differentiation model determination process using doublecontracting process upon selecting a differentiation model and features.The differentiation model determination process in FIG. 79 includes thesame processes up to 1) quantification of feature differentiationproperties, 2) ranking of features, and 3) hierarchical differentiationanalysis with features included in order from the top feature.Meanwhile, approximation of a change in differentiation accuracy using asigmoid function or the like to select an economical differentiationmodel is newly included as of the final differentiation model selection.All numerical values in the figure are exemplary.

FIG. 22 shows a change in monotonically increasing sigmoid andmonotonically decreasing sigmoid features. A shows the absolute meanamplitude, and B shows the frequency power. C shows frontal-parietalamplitude correlation (also known as potential correction), and D showsmultiscale entropy (MSE). Monotonically increasing sigmoid: has aninflection point at monotonically increasing sigmoid feature: levels 5to 6 in A, and the feature increased. Monotonically decreasing sigmoid:has an inflection point at monotonically decreasing sigmoid feature:levels 5 to 6 in C and D, and the feature decreased.

FIG. 23 is another embodiment of “discrete feature” comprising thesigmoidal feature shown in FIG. 22. Prior to pain monitoring, pain teststimulation is applied multiple times to an object to find thedistribution property of features for no pain and having pain. FIG. 23shows a schematic diagram when curve fitting using a histogram offeatures. The numerical value where distributions of samples for no painand having pain intersect is used as a differentiation threshold value.Samples less than the threshold value are converted to a category scaleof “−1” and samples that are greater than the threshold value areconverted to a category scale of “1”. Poincare distribution or the likecan also be used as the sample distribution for determining such athreshold value.

FIG. 24 is a schematic diagram of a differentiation apparatus with afeature contracting process. As of the model creation (white arrow), adifferentiation function matching the number of classification levels iscreated and fitted to each feature, and a feature is selected with aweighting coefficient such as R square value to create a differentiationmodel. This information is stored in a feature extraction unit and adifferentiation estimation unit. A pain level is differentiated andestimated online in actual pain monitoring (black arrow).

FIG. 25 is an example of a flow chart showing the flow in an embodimentof the present disclosure.

FIG. 26 is an example of a block diagram showing the functionalconfiguration in an embodiment of the present disclosure.

FIG. 27 is an example of a block diagram showing the functionalconfiguration in an embodiment of the present disclosure.

FIG. 28 shows the flow of 4 class LSTM analysis.

FIG. 29 shows the analysis condition for 4 class LSTM analysis.

FIG. 30A shows raw data obtained in Example 1.

FIG. 30B shows the off-line chronological analysis result obtained inExample 1.

FIG. 30C shows raw data obtained in Example 1.

FIG. 30D shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30E shows the raw data obtained in Example 1.

FIG. 30F shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30G shows the raw data obtained in Example 1.

FIG. 30H shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30I shows the raw data obtained in Example 1.

FIG. 30J shows the off-line chronological analysis result obtained inExample 1.

FIG. 30K shows the raw data obtained in Example 1.

FIG. 30L shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30M shows the raw data obtained in Example 1.

FIG. 30N shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30O shows the raw data obtained in Example 1.

FIG. 30P shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30Q shows the raw data obtained in Example 1.

FIG. 30R shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 30S shows the raw data obtained in Example 1.

FIG. 30T shows the off-line chronological analysis result of the rawdata obtained in Example 1.

FIG. 31 shows the entire image of the analysis of Example 2.

FIG. 32 shows an image of the stage of augmenting data of naive (N=7)and day28 (N=8).

FIG. 33 shows an image of the stage of using a created model to carryout fitting and predicting a pain score.

FIG. 34 shows the result of Example 2.

FIG. 35A shows a conceptual diagram of an analysis method using thesample augmentation method (OLD).

FIG. 35B shows a conceptual diagram of an analysis method using thesample augmentation method (OLD).

FIG. 35C shows a conceptual diagram of an analysis method using thesample augmentation method (OLD).

FIG. 35D shows a conceptual diagram of an analysis method using thesample augmentation method (OLD).

FIG. 36A shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36B shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36C shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36D shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36E shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36F shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36G shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 36H shows the result of using a model created in leave-one-out(n−1) data and differentiating one sample that has been left out 1000times.

FIG. 37A shows an example of gradual heat stimulation of (1) of anexperimental trial, an example of a COVAS template corresponding theretoand an example of a sorted COVAS template in which the COVAS template issorted in ascending order from the minimum value 0 to maximum value 100.

FIG. 37B shows the range of 19 types of standardization parameters cutout from the sorted COVAS template and 10 models corresponding to 10types of standardization parameters.

FIG. 37C shows the result of Example 4.

FIG. 38A shows 10 types of standardization parameters cut out from thesorted COVAS template and 10 models corresponding to 10 types ofstandardization parameters.

FIG. 38B shows the result of Example 5.

DESCRIPTION OF EMBODIMENTS

The present disclosure is explained hereinafter. Throughout the entirespecification, a singular expression should be understood asencompassing the concept thereof in the plural form, unless specificallynoted otherwise. Thus, singular articles (e.g., “a”, “an”, “the”, andthe like in the case of English) should also be understood asencompassing the concept thereof in the plural form, unless specificallynoted otherwise. The terms used herein should also be understood asbeing used in the meaning that is commonly used in the art, unlessspecifically noted otherwise. Thus, unless defined otherwise, allterminologies and scientific technical terms that are used herein havethe same meaning as the general understanding of those skilled in theart to which the present disclosure pertains. In case of acontradiction, the present specification (including the definitions)takes precedence.

Definition

The terms and the general technologies used herein are first explained.

(Information Processing Related Matters)

As used herein, “machine learning” refers to a technology for impartinga computer the ability to learn without explicit programming. This is aprocess of improving a function unit's own performance by acquiring newknowledge/skill or reconfiguring existing knowledge/skill. Most of theeffort required for programming details can be reduced by programming acomputer to learn from experience. In the machine learning field, amethod of constructing a computer program that enables automaticimprovement from experience has been discussed. Data analysis/machinelearning plays a role in elemental technology that is the foundation ofintelligent processing along with field of the algorithms. Generally,data analysis/machine learning is utilized in conjunction with othertechnologies, thus requiring the knowledge in the cooperating field(domain specific knowledge; e.g., medical field). The range ofapplication thereof includes roles such as prediction (collect data andpredict what would happen in the future), search (find a notable featurefrom the collected data), and testing/describing (find relationship ofvarious elements in the data). Machine learning is based on an indicatorindicating the degree of achievement of a goal in the real world. Theuser of machine learning must understand the goal in the real world. Anindicator that improves when an objective is achieved needs to beformularized. Machine learning has the opposite problem that is anill-posed problem for which it is unclear whether a solution is found.The behavior of the learned rule is not definitive, but is stochastic(probabilistic). Machine learning requires an innovative operation withthe premise that some type of uncontrollable element would remain. Thepresent disclosure can be considered as a solution to such problem uponcarrying out machine learning in an environment in which the number ofsamples such as biological information is limited. It is useful for auser of machine learning to sequentially select data or information inaccordance with the real world goal while observing performanceindicators during training and operation.

Linear regression, logistic regression, support vector machine, or thelike can be used for machine learning, and cross validation (CV) can beperformed to calculate differentiation accuracy of each model. Afterranking, a feature can be increased one at a time for machine learning(linear regression, logistic regression, support vector machine, or thelike) and cross validation to calculate differentiation accuracy of eachmodel. A model with the highest accuracy can be selected thereby. Anymachine learning can be used herein. Linear, logistic, support vectormachine (SVM), or the like can be used as supervised machine learning.

Machine learning uses logical reasoning. There are roughly three typesof logical reasoning, i.e., deduction, induction, and abduction as wellas analogy. Deduction, under the hypothesis that Socrates is a human andall humans die, reaches a conclusion that Socrates would die, which is aspecial conclusion. Induction, under the hypothesis that Socrates woulddie and Socrates is a human, reaches a conclusion that all humans woulddie, and determines a general rule. Abduction, under a hypothesis thatSocrates would die and all humans die, arrives at Socrates is a human,which falls under a hypothesis/explanation. However, it should be notedthat how induction generalizes is dependent on the premise, so that thismay not be objective. Analogy is a probabilistic logical reasoningmethod which reasons that if object A has 4 features and object B hasthree of the same features, object B also has the remaining one featureso that object A and object B are the same or similar and close.

Impossible has three basic principles, i.e., impossible, very difficult,and unsolved. Further, impossible includes generalization error, no freelunch theorem, and ugly duckling theorem and true model observation isimpossible, so that this is impossible to verify. Such an ill-posedproblem should be noted.

Feature/attribute in machine learning represents the state of an objectbeing predicted when viewed from a certain aspect. A featurevector/attribute vector combines features (attributes) describing anobject being predicted in a vector form.

As used herein, “model” or “hypothesis” are used synonymously, which isexpressed using mapping describing the relationship of inputtedprediction targets to prediction results, or a mathematical function orBoolean expression of a candidate set thereof. For learning with machinelearning, a model considered the best approximation of the true model isselected from a model set by referring to training data.

Examples of models include generation model, identification model,function model, and the like. Models show a difference in the directionof classification model expression of the mapping relationship betweenthe input (object being predicted) x and output (result of prediction)y. A generation model expresses a conditional distribution of output ygiven input x. An identification model expresses a joint distribution ofinput x and output y. The mapping relationship is probabilistic for anidentification model and a generation model. A function model has adefinitive mapping relationship, expressing a definitive functionalrelationship between input x and output y. While identification issometimes considered slightly more accurate in an identification modeland a generation model, there is basically no difference in view of theno free lunch theorem.

Model complexity: the degree of whether mapping relationship of anobject being predicted and prediction result can be described in moredetail and complexity. Generally, more training data is required for amodel set that is more complex.

If a mapping relationship is expressed as a polynomial equation, ahigher order polynomial equation can express a more complex mappingrelationship. A higher order polynomial equation is considered a morecomplex model than a linear equation.

If a mapping relationship is expressed by a decision tree, a deeperdecision tree with more nodes can express a more complex mappingrelationship. Therefore, a decision tree with more nodes can beconsidered a more complex model than a decision tree with less nodes.

Classification thereof is also possible by the principle of expressingthe corresponding relationship between inputs and outputs. For aparametric model, the distribution or shape of the function iscompletely determined by parameters. For a nonparametric model, theshape thereof is basically determined from data. Parameters onlydetermine smoothness.

Parameter: an input for designating one of a set of functions ordistribution of a model. It is also denoted as Pr[y|x; θ], y=f(x; θ), orthe like to distinguish from other inputs.

For a parametric model, the shape of a Gaussian distribution isdetermined by mean/variance parameters, regardless of the number oftraining data. For a nonparametric model, only the smoothness isdetermined by the number of bin parameter in a histogram. This isconsidered more complex than a parametric model.

For learning in machine learning, a model considered the bestapproximation of the true model is selected from a model set byreferring to training data. There are various learning methods dependingon the “approximation” performed. A typical method is the maximumlikelihood estimation, which is a standard of learning that selects amodel with the highest probability of producing training data from aprobabilistic model set.

Maximum likelihood estimation can select a model that best approximatesthe true model. KL divergence to the true distribution becomes small forgreater likelihood. There are various types of estimation that vary bythe type of form for finding a parameter or estimated prediction value.Point estimation finds only one value with the highest certainty.Maximum likelihood estimation, MAP estimation, and the like use the modeof a distribution or function and are most often used. Meanwhile,interval estimation is often used in the field of statistics in a formof finding a range within which an estimated value falls, where theprobability of an estimated value falling within the range is 95%.Distribution estimation is used in Bayesian estimation or the like incombination with a generation model introduced with a prior distributionfor finding a distribution within which an estimated value falls.

In machine learning, over-training (over-fitting) can occur. Withover-training, empirical error (prediction error relative to trainingdata) is small, but generalization error (prediction error relative todata from a true model) is large due to selecting a model that isoverfitted to training data, such that the original objective oflearning cannot be achieved. Generalization errors can be divided intothree components, i.e., bias (error resulting from a candidate model setnot including a true model; this error is greater for a more simplemodel set), variance (error resulting from selecting a differentprediction model when training data is different; this error is greaterfor a more complex model set), and noise (deviation of a true model thatcannot be fundamentally reduced, independent of the selection of a modelset). Since bias and variance cannot be simultaneously reduced, theoverall error is reduced by balancing the bias and variance. Since lesstraining data tends to cause overlearning, the possibility ofoverlearning may be reduced by using the sample augmentation of thepresent disclosure.

As used herein, “contract” refers to reducing or consolidatingvariables, i.e., features. For example, factor analysis refers toexplaining, when there are a plurality of variable, the relationshipbetween a plurality of variables with a small number of potentialvariables by assuming that there is a constituent concept affecting thevariables in the background thereof. This is a form of conversion to asmall number of variables, i.e., contracting. The potential variablesexplaining the constituent concept are referred to as factors. Factoranalysis contracts variables that can be presume to have the samefactors in the background to create new quantitative variables. Thesample augmentation of the present disclosure can be used aftercontracting features with respect to a sample, which enablesaugmentation of a sample in a state in which the features arecontracted.

As used herein, “differentiation function” is a numerical sequence,i.e., a function, created to match the arrangement of samples to bedifferentiated by assigning continuous numerical values to the number oflevels to be differentiated. For example, if samples to bedifferentiated are arranged to match the levels when there are twodifferentiation levels, the numerical sequence thereof, i.e.,differentiation function, is generated, for example, to have a form of asigmoid function. For three or more levels, a step function can be used.A model approximation index numerically represents the relationshipbetween a differentiation function and differentiation level of samplesto be differentiated. When a difference therebetween is used, the rangeof fluctuation is controlled. A smaller absolute value of a value ofdifference indicates higher approximation. When correlation analysis isperformed, a higher correlation coefficient (r) indicates higherapproximation. When regression analysis is used, a higher R² value isdeemed to have higher approximation.

As used herein, “weighting coefficient” is a coefficient that is set sothat an important element is calculated as more important in thecalculation of the present disclosure, including approximationcoefficients. For example, a coefficient can be obtained byapproximating a function to data, but the coefficient itself only has adescription indicating the degree of approximation. When coefficientsare ranked or chosen/discarded on the basis of the magnitude or thelike, a difference in contribution within the model is provided to aspecific feature, so that this can be considered a weightingcoefficient. A weighting coefficient is used in the same meaning as anapproximation index of a differentiation function. Examples thereofinclude R² value, correlation coefficient, regression coefficient,residual sum of squares (difference in feature from differentiationfunction), and the like.

As used herein, “differentiation function model” refers to a model of afunction used for differentiation of pain or the like. Examples thereofinclude, but are not limited to, sigmoid function and step function.

(Brainwave Related Matters)

As used herein, “object” refers to any entity subjected to machinelearning. However, when referred to regarding a brainwave, “object” isused synonymously with patient and subject and refers to any organism oranimal which is subjected to the technology in the disclosure such aspain measurement and brainwave measurement. An object is preferably, butis not limited to, humans. As used herein, an object may be referred toan “object being estimated” when estimating pain, but this has the samemeaning as object or the like. There may be a plurality of “objects”. Insuch a case, each individual may be referred to as a “sample” (ofobjects).

As used herein, “brainwave” has the meaning that is commonly used in theart and refers to a current generated by a difference in potential dueto neurological activity of the brain when a pair of electrodes isplaced on the scalp. Brainwave encompasses electroencephalogram (EEG),which is obtained from deriving and recording temporal changes in thecurrent. A wave with an amplitude of about 50 μV and a frequency ofapproximately 10 Hz is considered the primary component at rest. This isreferred to as an a wave. During mental activity, a waves are suppressedand a fast wave with a small amplitude of 17 to 30 Hz appears, which isreferred to as a R wave. During a period of shallow sleep, a wavesgradually decrease and e waves of 4 to 8 Hz appear. During a deep sleep,6 waves of 1 to 4 Hz appear. These brainwaves can be expressed by aspecific amplitude, frequency, complexity index, correlation, or thelike. Brainwaves can be represented by a specific, amplitude andfrequency or analysis of amplitude in the present disclosure.

As used herein, “brainwave data” is any data related to brainwaves (alsoreferred to as “amount of brain activity”, “brain feature”, or thelike), such as amplitude data (EEG amplitude), frequency property, orthe like. “Analysis data” from analyzing such brainwave data can be usedin the same manner as brainwave data, so that such data is collectivelyreferred to as “brainwave data or analysis data thereof” herein.Examples of analysis data include mean amplitude and peak amplitude(e.g., Fz, Cz, C3, C4), frequency power (e.g., Fz (δ), Fz (θ), Fz (α),Fz (β), Fz (γ), Cz (δ), Cz (θ), Cz (α), Cz (β), Cz (γ), C3 (δ), C3 (θ),C3 (α), C3 (β), C3 (γ), C4 (δ), C4 (θ), C4 (α), C4 (β), and C4 (γ)) andthe like of brainwave data. Of course, this does not exclude other datacommonly used as brainwave data or analysis data thereof. For example,raw data sampled out for a certain period of time, when used fordifferentiation, is also a feature, so this can also be used in thepresent disclosure.

As used herein, “brainwave feature” or “feature of brainwave” refers toany feature of a brainwave, encompassing “brainwave data or analysisdata thereof” such as amplitude, interrelation of brainwave features,frequency power, and complexity index. As examples thereof, theamplitude can comprise an amplitude distribution property value such asa mean amplitude (e.g., absolute mean amplitude, relative meanamplitude, or the like), an amplitude median value, an amplitude mode,an amplitude maximum value, a peak amplitude, or a quartile amplitude,the interrelation of brainwave features can comprise potentialcorrelation (e.g., frontal-parietal potential correlation (a correlationcoefficient, a partial correlation coefficient, Connectivity, Causality,and subtypes thereof)) or phase synchronization between electrodes(e.g., coherence, Phase locking value, and subtypes thereof), thefrequency power can comprise a spectral density, a power spectrum, or asubtype thereof, and the complexity index can comprise at least oneselected from entropy (e.g., multiscale entropy (MSE), sample entropy,self entropy, mean entropy, joint entropy, relative entropy, conditionalentropy, and the like), and a biological potential feature manifested inassociation with an event in conjunction with occurrence of pain (eyemovement potential reflecting eye movement such as a blink reflex or thelike)

As used herein, “amplitude data” is one type of “brainwave data” andrefers to data for amplitudes of brainwaves. This is also referred to assimply “amplitude” or “EEG amplitude”. Since such amplitude data is anindicator of brain activity, such data can also be referred to as “brainactivity data”, “amount of brain activity”, or the like. Amplitude datacan be obtained by measuring electrical signals of a brainwave and isindicated by potential (can be indicated by μV or the like). Amplitudedata that can be used include, but are not limited to, mean amplitude.

As used herein, “frequency power” expresses frequency components of awaveform as energy and is also referred to as power spectrum. Frequencypower can be calculated by extracting and calculating frequencycomponents of a signal embedded in a signal contained in noise within atime region by utilizing fast Fourier transform (FFT) (algorithm forcalculating discrete Fourier transform (DFT) on a computer at highspeeds). FFT on a signal can, for example, use the function periodgramin MATLAB to normalize the output thereof and calculate the powerspectrum density PSD or power spectrum, which is the source ofmeasurement of power. PSD indicates how power of a time signal isdistributed with respect to frequencies. The unit thereof is watt/Hz.Each point in PSD is integrated over the range of frequencies where thepoint is defined (i.e., over the resolution bandwidth of PSD) tocalculate the power spectrum. The unit of a power spectrum is watt. Thevalue of power can be read directly from power spectrum withoutintegration over the range of frequencies. PSD and power spectrum areboth real numbers, so that no phase information is included. In thismanner, frequency power can be calculated with a standard function inMATLAB. As the analysis method, time frequency analysis can be used asexemplified in FIG. 11. For example, temporal change in frequency powercan be found, for example, by determining a unit of time such as 1second and shifting the point. As shown in FIG. 11, this elucidates thedelay property of thermal pain. For example, a time segment where painis sharp (e.g., 5 seconds after application of stimulation andthereafter) can be identified, and a feature that can be categoricallydistinguished from “delayed pain feature” among frequency features canbe created.

As used herein, “complexity” refers to a situation where logicallypossible connection relationships among various elements are excessive,and one relationship must be selected therefrom. When used in thecontext of brainwaves, this refers to a state where the possibleconnection relationship of each brainwave is excessive. An index thereofis referred to as a “complexity index”.

As used herein, “complexity index” refers to a scale of complex andsuperficially irregular appearing behavior due to the large number ofconstituent elements or nonlinearity. A complexity index can berepresented with entropy or the like. Entropy refers to the scale ofdisorderliness of a system and refers to the mean value of the amount ofinformation that is communicated by knowing which event has occurredfrom a limited complete event system. In informatics, entropy is alsoreferred to as the amount of information, which is an indicator ofcomplexity of activity. Thus, complexity is broader than the concept ofentropy in the chaotic sense. Examples of entropy include, but are notlimited to, multiscale entropy (MSE), sample entropy, self entropy, meanentropy, joint entropy, relative entropy, conditional entropy, and thelike.

Multiscale entropy (MSE) is an analysis method that has drawn attentionas a new nonlinear analysis method, which has overcome problems ofexisting nonlinear analysis methods (data stability, i.e., state where aproperty of the entire data (variance or the like) is not locallyreproduced). MSE is vulnerable to artifacts and requires data from anextended period of time with high resolution, but was further improvedupon from approximate entropy (ApEn; Pincus S M. Approximate entropy asa measure of system complexity. Proc Natl Acad Sci USA 1991;88:2297-2301) and an improved version thereof, i.e., sample entropy(SampEn; Richman J S. Moorman J R. Physiological time-series analysisusing approximate entropy and sample entropy. Am J Physiol Heart CircPhysiol 2000; 278: 2039-2049) as a practical nonlinear analysis methodto overcome the fact that measurements meeting such conditions arechallenging for actual measurement data in clinical settings. MSE wasdeveloped by Costa et al (Costa M. Goldberger A L. Peng C K. Multiscaleentropy analysis of complex physiologic time series. Phys Rev Lett 2002;89: 068102). MSE is found by reconstructing the original data fromfinding the arithmetic mean so that the data does not overlap andcalculating each of ApEn/SampEn of the reconstructed data with aplurality of number of additions (time axis). With a low number ofadditions, complexity of a high frequency band is represented. With ahigh number of additions, complexity of a low frequency band isrepresented. Therefore, MSE analysis enables nonlinear extraction, whichwas challenging with existing nonlinear analysis methods. For example,Busa & Emmerik have recently published a report for brainwaves (Journalof Sport and Health Science Volume 5, Issue 1, March 2016, Pages 44-51).

As used herein, “interrelation of brainwave features” refers to theinterrelation of two or more brainwave features. Any brainwave featuremay be used as long as the feature is brainwave data or analysis datathereof. Examples thereof include, but are not limited to, amplitude(including mean amplitude and the like), frequency power, potential,complexity index (including MSE and the like), and the like.

As used herein, “interrelation” refers to any relationship between twofeatures. Interrelation is a broad concept including the relationship ofdifferent features of the same electrode, relationship of the samefeatures at different times, and the like, including correlation. Thereis a phase synchronization indicator, which retrieves multiple phases ofactivity to find synchronicity between periods. This is encompassed by abroadly defined concept of interrelation. In this manner, interrelationdoes not use a correlation coefficient, but encompasses the same type ofrelational indicator.

As used herein, “correlation” generally indicates a concept ofassociation between two or more variable amounts or a value thereof inmathematical statistics and biological statistics. As an example ofcorrelation, the statistical scale can be represented by a correlationcoefficient (r, ρ, or the like). The value thereof is between −1 and +1.A value close to +1 means positive correlation, a value close to −1means negative correlation, and a value close to 0 meansnon-correlation. For example, human body length/body weight has apositive correlation with a certain r value. If there is a positive ornegative correlation, the functional relationship (empirical formula)between variables can be found in a form of a regression line, and thismethodology can be expanded to nonlinear regression. Qualitativecorrelation for only large or small (+1 or −1) for each variable can becalled association. One characteristic of the sample augmentation of thepresent disclosure is that the correlation can also be retained byaugmentation.

In one embodiment, interrelation encompasses correlation as well asindicators that cannot be considered correlation such as a phasesynchronization indicator for finding synchronicity between periods.Examples of narrowly defined correlation, i.e., various forms ofcorrelation (synchronicity, unrelatedness, delay, positive/negative,similarity, and match) include temporal correlation, spatialcorrelation, spatiotemporal synchronicity, spatial relationship orconnectivity, unrelatedness or uncorrelatedness, delay or breakdown intemporal correlation, positive/negative or correlated property,similarity or level of correlation coefficient, and a match or completecorrelation. In this manner, it can be understood that synchronicity istemporal correlation, connectivity is a spatial (e.g., parts of brain)relationship, unrelatedness is uncorrelatedness, delay is breakdown intemporal correlation, positive/negative is correlated property,similarity is having a high correlation coefficient, and match iscomplete correlation.

As used herein, “pain” refers to a sensation that is generated asstimulation, generally upon intense injury such as damage/inflammationto a body part. Pain is not a disease but is a symptom. The statethereof is determined by the combination of three main properties, i.e.,central nervous, nociceptive, and neuropathic pain. Acute pain andchronic pain are distinguished, which are different in terms of theassociated cerebral site network (connectivity). Chronic pain issometimes subjectively reported as painful when in fact it is notpainful. Chronic pain includes psychogenic factors that cannot beexplained by sensational intensity of pain stimulation.

In humans, pain is encompassed by common sensations as a sensationaccompanying strong unpleasant feeling. In addition, cutaneous pain andthe like also has an aspect as an external receptor to a certain degree,which plays a role in determining the quality such as hardness,sharpness, hotness (thermal pain), coldness (cold pain), or spiciness ofan external object in cooperation with other skin sensation or taste.The sensation of pain of humans can occur at almost any part of the body(e.g., pleura, peritoneum, internal organs (visceral pain, excluding thebrain), teeth, eyes, ears, and the like) other than the skin and mucousmembrane, which can all be sensed as a brainwave or a change thereof inthe brain. Additionally, internal sensation of pain represented byvisceral pain is also encompassed by sensation of pain. Theaforementioned sensation of pain is referred to as somatic pain relativeto visceral pain. In addition to somatic pain and visceral pain,sensation of pain called “referred pain”, which is a phenomenon wherepain is perceived at a surface of a site that is different from a sitethat is actually damaged, is also reported. The present disclosureprovides a methodology of expressing a temporal change in such variouspain types as a trend and monitoring subjective pain levels, and othermethodologies described herein.

For sensation of pain, there are individual differences in sensitivity(pain threshold), as well as qualitative difference due to a differencein the receptor site or how a pain stimulation occurs. Sensation of painis classified into dull pain, sharp pain, and the like, but sensation ofpain of any type can be measured, estimated, and classified in thisdisclosure. The disclosure is also compatible with fast sensation ofpain (A sensation of pain), slow sensation of pain (B sensation ofpain), (fast) topical pain, and (slow) diffuse pain. The presentdisclosure is also compatible with abnormality in sensation of pain suchas hyperalgesia. Two nerve fibers, i.e., “Aδ fiber” and “C fiber”, areknown as peripheral nerves that transmit pain. For example, when a handis hit, the initial pain is transmitted as sharp pain from a clearorigin (primary pain: sharp pain) by conduction through the Aδ fiber.Pain is then conducted through the C fiber to feel throbbing pain(secondary pain; dull pain) with an unclear origin. Pain is classifiedinto “acute pain” lasting 4 to 6 weeks or less and “chronic pain”lasting 4 to 6 weeks or more. Pain is an important vital sign along withpulse, body temperature, blood pressure, and breathing, but is difficultto express as objective data. Representative pain scales VAS (visualanalogue scale) and faces pain rating scale are subjective evaluationmethods that cannot compare pain between patients. Meanwhile, theinventors have focused on brainwaves which are hardly affected by theperipheral circulatory system as an indicator for objectively evaluatingpain, arriving at the conclusion that pain can be differentiated andclassified by observing the change during latency/amplitude in responseto pain stimulation and performing trend analysis. In particular,instantaneous pain and throbbing sustained pain can also bedistinguishable by the trend analysis of the present disclosure. Sinceinstantaneous pain is pain during a short time segment, associated brainactivity can decrease if a time direction averaging method over at leastseveral tens of seconds is used in trend analysis (e.g., significantcorrelation with pain evaluation is not observed). Meanwhile, sustainedpain is continuous, so that significant correction with pain evaluationcan be rather strengthened by a time direction averaging method. Theinventors focused on brainwaves that are less susceptible to the effectof the peripheral circulatory system as an indicator for objectiveevaluation of pain. Observation of the change during latency/amplitudein response to pain stimulation lead to classification of types of pain(comfort/discomfort). Instantaneous stimulation and sustainedstimulation can also be classified in this manner.

One of the important points of the present disclosure is in the abilityto distinguish whether pain is pain “requiring therapy”, rather than theintensity in itself. Therefore, it is important that “pain” can beclearly categorized based on the concept of “therapy”. For example, thisleads to “qualitative” classification of pain such as“pleasant/unpleasant” or “unbearable”. For example, the position of a“pain index”, baseline, and the relationship thereof can be defined. Inaddition to a case of n=2, cases where n=3 or greater can also beenvisioned. When n is 3 or greater, pain can be separated into “notpainful”, “comfortable pain”, and “painful”. For example, pain can bedifferentiated as “unbearable, need therapy”, “moderate”, or “painful,but not bothersome”. When the trend analysis of the present disclosureis used, “unbearable” and “painful but bearable” pain can bedistinguished by identifying a threshold value for long/short durationof a signal associated with strong pain. For example, when the sampleaugmentation of the present disclosure is used on a signal that canseparate whether or not a pain needs therapy, machine learning can becarried out to find out whether or not a pain needs such therapy, whilebeing accurately retained.

As used herein, “subjective pain sensation level” refers to the level ofsensation of pain of an object, and can be expressed by conventionaltechnology such as computerized visual analog scale (COVAS) or otherknown technologies such as Support Team Assessment Schedule (STAS-J),Numerical Rating Scale (NRS), Faces Pain Scale (FPS), Abbey pain scale(Abbey), Checklist of Nonverbal Pain Indicators (CNPI),Non-communicative Patient's Pain Assessment Instrument (NOPPAIN),Doloplus 2, or the like.

As used herein, “pain classifier” refers to the value or range thereofof the brainwave data (e.g., amplitude) or analysis thereof specified toclassify the type of pain. In the present disclosure, a portion,apparatus, or device generating the “pain classifier” (and thuspredicting the pain) may be referred to as “pain classification tool”,“pain prediction tool”, or the like. The present disclosure can carryout, but not limited to, stimulation of an object being estimated anddetermination of data such as amplitude data of brainwave obtainedtherefrom using, for example, an inflection point or the like based on aspecific pain function (e.g., also referred to as a liner function orsigmoid curve specific to the object being estimated) obtained byplotting stimulation intensity thereof or a subjective pain sensationlevel corresponding to the stimulation intensity and for application andfitting to a pain function. After being generated, a pain classifier canbe improved by carrying out calibration. The pain classifier may also beexpressed as pain predictor and the like, which all have the samemeaning. It is possible to distinguish between “change within an intensepain level” and “qualitative change showing a mild pain level deviatedfrom an intense pain level” with the use of the “pain classifier”. Whenthere is a deviation reaction that exceeds a change within an intensepain level, distinguishment from the change within an intense pain levelis possible by using the pain classifier of the present disclosure. Inthe case of a change within an intense pain level, this is not an errorbut a change that can be distinguished, wherein when this is exceeded,processing as a deviation reaction may be carried out. For example, whena signal such as a brainwave signal that can classify the type of painis augmented using the sample augmentation of the present disclosure formachine learning, such pain classifier can also be accuratelycalculated.

As used herein, “pain function” refers to a term expressing thecorrelation between a pain level and a stimulation level with anumerical formula function of a dependent variable (variable Y) and anindependent variable (variable X), which expresses the relationshipthereof as a function based on a “broadly defined” linearity between abrainwave or analysis data thereof (including, for example, amplitude)and pain, which were unraveled by the inventors of the presentinvention. Because of this relationship, it is possible to (i) estimatethat, when the first brainwave data or analysis data thereof (including,for example, amplitude) is greater than the second brainwave data oranalysis data thereof (including, for example, amplitude), a first paincorresponding to the first brainwave data is greater than a second paincorresponding to the second brainwave data, and (ii) estimate that, whenthe first brainwave data or analysis data thereof (including, forexample, amplitude) is less than the second brainwave data or analysisdata thereof (including, for example, amplitude), the first pain is lessthan the second pain. It is understood that any function would be withinthe scope of the pain function as long as the function can express sucha matter. An example of such a pain function can include a linearfunction or a sigmoid function, and a more specific example can includea linear function with the range of inflection linearly approximated, ora comprehensive sigmoid function encompassing the linear function. Otherthan amplitude, the linearity can be referred to in a frequency orwavelet processing value as long as it is a brainwave feature, whereinthe linearity in the range of inflection can be found not only in thebrainwave feature but also in subjective evaluation. For example, whenbrainwave data or analysis data thereof is augmented using the sampleaugmentation of the present disclosure for machine learning, it ispossible to accurately calculate such pain function.

As used herein, “stimulation” refers to anything that causes some typeof a reaction to an object. If the object is an organism, stimulationrefers to a factor resulting in a temporarily change in thephysiological activity of the organism or a portion thereof.

Events related to sensation of pain presented as specific examples of“stimulation” includes any stimulation that can cause sensation of pain.Examples thereof include electrical stimulation, cold stimulation,thermal stimulation, physical stimulation, chemical stimulation, and thelike. In the present disclosure, stimulation can be any stimulation.Evaluation of stimulation can be matched with subjective pain sensationlevels using, for example, conventional technology such as computerizedvisual analog scale (COVAS) or other known technologies such as SupportTeam Assessment Schedule (STAS-J), Numerical Rating Scale (NRS), FacesPain Scale (FPS), Abbey pain scale (Abbey), Checklist of Nonverbal PainIndicators (CNPI), Non-communicative Patient's Pain AssessmentInstrument (NOPPAIN), Doloplus 2, or the like. Examples of values thatcan be employed as stimulation intensity include nociceptive threshold(threshold for generating neurological impulses in nociceptive fiber),pain detection threshold (intensity of nociceptive stimulation that canbe sensed as pain by humans), pain tolerance threshold (strongeststimulation intensity among nociceptive stimulation that isexperimentally tolerable by humans), and the like.

As used herein, “classification” of pain can be performed from variousviewpoints. Representative examples include classification by whetherpain is “painful” or “not painful” for the object being estimated, but amethodology of classification for pain felt by whether pain is strongpain or weak pain, or “bearable” pain or “unbearable” pain can beenvisioned. Other examples include a methodology of classificationbetween “painful and unpleasant” and “painful but pleasant”. Forexample, when a signal that can classify the intensity of pain isaugmented using the sample augmentation of the present disclosure andundergoes machine learning, it is possible to chronologicallydifferentiate/estimate whether an object feels unbearable strong pain orweak pain by observing monotonous increase or monotonous decrease.

As used herein, “pain index” refers to an index obtained byappropriately processing brainwave data or analysis data thereof. Aslong as an explanation is provided for the provided index, anyprocessing method can be used, but a methodology that can visualize andtrack a property (trend) of temporally sustained or changing pain isimportant. A pain index can be calculated by the machine learningdescribed in herein. A pain index can also be referred to as a “painlevel index”. The term “pain indicator” refers to subjective evaluation,stimulation intensity, associated brain feature, or the like.

As used herein, “baseline” refers to a standard or reference forfacilitating the reading of pain levels, such as a feature associatedwith strong pain level at the start of monitoring, mean value ornormalized value thereof, or a method using a pain index as zero, and acalculated numerical value. The sample augmentation of the presentdisclosure cab also be used for baseline calculation.

As used herein, “headset” refers to equipment used for obtainingbrainwaves from the head. A headset can have any shape. Any obtainingmethod can be used as long as brainwaves can be directly or indirectlyobtained. A headset can be preferably shaped to be worn on the head, butthe shape is not limited thereto. Examples thereof include those in ashape of a wireless head gear as well as existing shapes such as a hat,net, or band type headsets. With further improvement, the shape can beof any form, as long as brainwaves are obtained directly from the headvia electrodes such as a hair pin form. Brainwaves can also be obtainedwithout contact from the outside. The above forms can be collectivelycalled headsets.

As used herein, “base unit” refers to a part that obtains informationsuch as brainwave signals from a headset and performs action such asanalysis, differentiation, communication, and display. Abase unit cancomprises a process, which is configured mainly to extract aquantitative feature such as a brainwave feature from brain electricalactivity data (brainwave data, analysis data thereof or the like) of anobject, and further generate and apply a differentiation model,differentiate pain, and the like. A base unit may include an inputdevice for input into a memory device that is operably connected to aprocessor.

PREFERRED EMBODIMENTS

The preferred embodiments of the present disclosure are describedhereinafter. It is understood that the embodiments provided hereinafterare provided to facilitate better understanding of the presentdisclosure, so that the scope of the present disclosure should not belimited by the following descriptions. Thus, it is apparent that thoseskilled in the art can refer to the descriptions herein to makeappropriate modifications within the scope of the present disclosure. Itis also understood that the following embodiments of the presentdisclosure can be used individually or as a combination.

Each of the embodiments described below provides a comprehensive orspecific example. The numerical values, shapes, materials, constituentelements, positions of arrangement and connection forms of theconstituent elements, steps, order of steps, and the like in thefollowing embodiments are one example, which is not intended to limitthe Claims. Further, the constituent elements in the followingembodiments that are not recited in the independent claims showing themost superordinate concept are described as an optional constituentelement.

The present disclosure provides, in one aspect, a system 100 foraugmenting supervisory data used in machine learning.

FIG. 1 shows an example of a configuration of the system 100 foraugmenting supervisory data used in machine learning.

The system 100 comprises an obtaining means 110, a processor 120, amemory 130 and an output means 140.

The obtaining means 110 is configured to obtain a plurality ofsupervisory data. The obtaining means 110 obtains a plurality ofsupervisory data from outside the system 100. The obtaining means 110may be caused to, for example, obtain a plurality of supervisory datafrom a storage medium (e.g., database) inside a system 100 or connectedto a system 100, may be caused to obtain a plurality of supervisory datavia a network connected to the system 100, and may be caused to obtaindata detected using a detection means (not shown) that the system 100may comprise as the supervisory data. The detection means can detect anydata by any methodology. The detection means can, for example, detectreaction data by applying stimulation to an organism, wherein, upondoing so, the reaction data may be, for example, brainwave data. Forexample, when a system for generating the pain estimation modeldescribed below, an apparatus generating a pain classifier, or a systemgenerating a model for differentiating the pain of an object isconnected to the system 100, the obtaining means 110 may be caused toobtain a plurality of data obtained by a system for generating a painestimation model, an apparatus generating a pain classifier, or a systemgenerating a model for differentiation of the pain of an object from asystem for generating a pain estimation model, an apparatus generating apain classifier, or a system generating a model for differentiating thepain of an object as supervisory data.

Each of the plurality of supervisory data obtained by the obtainingmeans 110 may have a plurality of features.

The processor 120 implements the processing of the system 100 andcontrols the operation of the entirety of the system 100. The processor120 reads a program stored in a memory 130 and implements the program.This enables the system 100 to function as a system implementing adesired step. The processor 120 may be caused to carry out theprocessing of conversion into a form suitable for processing when thesupervisory data obtained by the obtaining means 110 is in a form thatis not suitable for processing. The processor 120 may be implemented bya single processor, or may be implemented by a plurality of processors.

The processor 120 may comprise a first processing means 121, a secondprocessing means 122 and a third processing means 123.

The first processing means 121 is configured to derive a covariancematrix from a plurality of supervisory data obtained by the obtainingmeans 110. Since each of a plurality of supervisory data obtained by theobtaining means 110 has a plurality of features, the first processingmeans 121 can express the plurality of supervisory data with an n×dmatrix when the number of the supervisory data (number of samples)obtained by the obtaining means 110 is set as n and the number offeatures comprised in each supervisory data is set as d. The firstprocessing means 121 can derive a covariance matrix from this n×dmatrix. In the covariance matrix derived from the first processing means121, variance of each feature is retained in a diagonal component, andcovariance between two features is retained in the other components.

The first processing means 121 may also be configured to calculate amean value for every feature from the plurality of supervisory dataobtained from the obtaining means 110. Upon doing so, the firstprocessing means 121 may be caused to, for example, subtract thecalculated mean value of each feature from the feature of the pluralityof supervisory data and derive a covariance matrix from the plurality ofsupervisory data in which the mean value of each feature has beensubtracted.

The second processing means 122 is configured to decompose a covariancematrix derived by the first processing means 121.

The second processing means 122 can, for example, decompose a covariancematrix into Q*Q′. In this regard, matrix Q′ is a transposed matrix ofmatrix Q. The second processing means can decompose a covariance matrixinto Q*Q′ by carrying out one of, for example, Cholesky decompositionand singular value decomposition. Cholesky decomposition is adecomposition of a matrix related to solving of a linear equationsystem, and the singular value decomposition is a decomposition of amatrix based on an eigenvalue or a concept associated thereto.

For example, a covariance matrix may be decomposed by Choleskydecomposition into an upper triangular matrix and a lower triangularmatrix which is a transposed matrix thereof.

For example, a covariance matrix may be decomposed into U*W*V′ bysingular value decomposition. Matrix U and matrix V are orthogonalmatrices, matrix W is a diagonal matrix and matrix V′ is a transposedmatrix of matrix V.

U*W*V′ obtained by singular value decomposition of a covariance matrixmay also be expressed with Q*Q′.

This is because, since W=W_*W_ would be achieved when W_=sqrt(W),

$\begin{matrix}{{U*W*V^{\prime}} = {U*W\;\_\;*W\;\_\;*V^{\prime}}} \\{= {U*W\;\_\;*\left( {V*W\;\_}\; \right)^{\prime}}}\end{matrix}$

would be achieved, and since U=V would be achieved when a covariancematrix is decomposed,

$\begin{matrix}{{U*W*V^{\prime}} = {U*W\;\_\;*\left( {U*W\;\_}\; \right)^{\prime}}} \\{= {Q*Q^{\prime}}}\end{matrix}$

would be achieved. In this regard, sqrt( ) is a function employing asquare root.

Eigenvalue decomposition is a singular value decomposition of when acovariance matrix is a square matrix, wherein the covariance matrix maybe decomposed into P*W*P′ by the eigenvalue decomposition. Matrix P isan orthogonal matrix, matrix W is a diagonal matrix and matrix P′ is atransposed matrix of matrix P. P*W*P′ obtained by eigenvaluedecomposition of a covariance matrix may also be expressed with Q*Q′.

The second processing means 122 may be caused to derive an uppertriangular matrix by, for example, carrying out LU decomposition or QRdecomposition instead of Cholesky decomposition. LU decomposition is ageneral form of Cholesky decomposition, which is a methodology ofdecomposing a matrix into an upper triangular matrix and a lowertriangular matrix. QR decomposition is a methodology of decomposing amatrix into an orthogonal matrix and an upper triangular matrix.

The third processing means 123 is configured to apply a random number toa matrix decomposed by the second processing means 122. The thirdprocessing means 123 can apply a random number by multiplying adecomposed matrix by a random number matrix. The random number may be,for example, a normal random number. The third processing means 123 canapply a random number to, for example, matrix Q or matrix Q′ decomposedby the second processing means 122. The third processing means 123 canapply a random number to, for example, matrix Q′ (upper triangularmatrix) decomposed by Cholesky decomposition, can apply a random numberto, for example, matrix Q′ (upper triangular matrix) decomposed by LUdecomposition, or can apply a random number to, for example, matrix Q′(upper triangular matrix) decomposed by QR decomposition.

The third processing means 123 may also be configured to add a meanvalue calculated by the first processing means 121 to a matrix to whicha random number has been applied.

A memory 130 stores a program required for implementation of processingof the system 100, data required for implementation of the program, andthe like. The memory 130 may store a program (e.g., program formaterializing the processing shown in FIGS. 3 to 5 described below) forcausing the processor 120 to carry out the processing for augmentationof supervisory data used for machine learning. The processor 120 maystore a program for causing the processor 120 to carry out theprocessing of learning a plurality of augmented supervisory data. Inthis regard, the method in which the program is stored in the memory 130may be any method. For example, the program may be preinstalled in thememory 130. Alternatively, the program may be installed in the memory130 by being downloaded through a network. In this case, the type of thenetwork may be any type. The memory 130 may be implemented by anystorage means.

The output means 140 is configured to enable output of data outside thesystem 100. The output means 140 can output the augmented supervisorydata. The form in which the output means 140 outputs the augmentedsupervisory data may be any form. For example, when the output means 140is a transmitter, output may be carried out by the transmittertransmitting the augmented supervisory data outside the system 100 via anetwork 500. For example, when the output means 140 is a data writingapparatus, the augmented supervisory data may be outputted by writingthe augmented supervisory data to a storage medium a database unit 200connected to the system 100. For example, when the below-mentionedsystem for generating a pain estimation model, apparatus generating apain classifier, or system generating a model for differentiation of thepain of an object is connected to the system 100, output may be carriedout by providing the augmented supervisory data to the system forgenerating a pain estimation model, apparatus generating a painclassifier, or system generating a model for differentiation of the painof an object. For example, the output means 140 may be caused to carryout conversion into a form that enables handling by a hardware orsoftware of an output destination of data or adjustment to a responsespeed that enables handling by a hardware or software of an outputdestination of data to output the data.

FIG. 2 shows another example of a configuration of the system 100 foraugmenting supervisory data used for machine learning. In FIG. 2, thesame reference numbers are provided to the elements that are same as theelements shown in FIG. 1, wherein the explanation is omitted. The system100′ shown in FIG. 2 is a configuration in which a fourth processingmeans 124 is added to the processor 120 of the system 100 shown inFIG. 1. The system 100′ can use principal component analysis to augmenta plurality of supervisory data.

In this example, it is preferable that the first processing means 121 becaused to calculate a mean value for every feature from a plurality ofsupervisory data obtained by the obtaining means 110 and subtract themean value of each feature that has been calculated from the features ofthe plurality of supervisory data. This is because this causes eachcomponent to be distributed around the origin of a principal componentspace when the result of principal component analysis is projected tothe principal component space. Augmentation of data in which eachcomponent is distributed around the origin enables augmentation of thedata while maintaining the relationship among the features.

In this example, the second processing means 122 can carry out principalcomponent analysis to a plurality of supervisory data to calculate theprincipal component space. In addition, the second processing means 122can calculate a principal component score and seek the standarddeviation of every feature from the principal component score byprojecting a plurality of supervisory data to the principal componentspace. The principal component score may correspond to a product inwhich the matrix of the plurality of supervisory data has been dividedwith the below-mentioned matrix V′.

In this example, the third processing means 123 can apply a randomnumber to, for example, a standard deviation calculated by the secondprocessing means 122.

The fourth processing means 124 is configured to carry out projectionconversion of the standard deviation to which a random number has beenapplied by the third processing means 123 from the principal componentspace to the original space.

The fourth processing means 124 can carry out projection conversion fromthe principal component space to the original space using, for example,the principal component coefficient of the principal component spacecalculated by the second processing means 122.

When the mean value of each feature is subtracted by the firstprocessing means 121, the fourth processing means 124 can add the meanvalue to the data in which projection conversion has been carried out tothe original space. This enables restoration of the information that isoriginally held by the plurality of supervisory data that has beensubtracted by the first processing means 121.

The above-mentioned processing of principal component analysis by thefirst processing means 121 to the fourth processing means 124 may beequivalent to decomposition of covariance matrix derived from aplurality of supervisory data into V*L*V′. In this regard, matrix L is adiagonal matrix consisting of an eigenvalue, matrix V is a matrix havinga corresponding eigenvector in the column and matrix V′ is a transposedmatrix of matrix V.

Upon doing so, the first processing means 121 can derive a covariancematrix from a plurality of supervisory data or a plurality ofsupervisory data in which the mean value of each feature has beensubtracted. The second processing means 122 can decompose a covariancematrix derived by the first processing means 121 into V*L*V′. Upon doingso, when it is defined that matrix M=sqrt(L), a diagonal component ofmatrix M corresponds to the standard deviation of the principalcomponent score. In this regard, sqrt( ) is a function employing asquare root. The third processing means 123 can apply a random value tomatrix M. For example, the third processing means 123 can apply a randomnumber by multiplying matrix M by a random number matrix. The fourthprocessing means 124 can return to the original space by carrying outprojection conversion of a random number to matrix M. The fourthprocessing means 124 can carry out projection conversion to the originalspace by, for example, multiplying matrix M to which a random number hasbeen applied by V′ from the right. For example, when the mean value ofeach feature is subtracted by the first processing means 121, the fourthprocessing means 124 can be caused to add the mean value to the matrix Mthat underwent projection conversion. This enables recovery ofinformation originally held by a plurality of supervisory datasubtracted by the first processing means 121.

In the above-described example, each constituent element of the system100 is provided within the system 100, but the present disclosure is notlimited thereto. Any of the constituent elements of the system 100 canbe provided outside or distal with respect to the system 100. Forexample, when each of the processor 120 and memory 130 is configuredwith separate hardware parts, each hardware part may be connected viaany network. Upon doing so, the type of the network may be any type.Each hardware part, for example, may be connected via a LAN, may bewirelessly connected, or may be wire connected. The system 100 is notlimited to a specific hardware configuration. For example, it is withinthe scope of the present disclosure that the processor 120 is configurednot by a digital circuit but by an analog circuit. The configuration ofthe system 100 is not limited to the discussion above as long as thefunction thereof can be materialized.

FIG. 3 shows an example of a processing 300 in a system 100 foraugmenting supervisory data used for machine learning.

The processing 300 enables augmentation of a plurality of supervisorydata put into the system 100 for use in machine learning.

In step S301, the obtaining means 110 of the processor 120 obtains aplurality of supervisory data. The obtaining means 110, for example,obtains a plurality of supervisory data from outside the system 100. Theobtaining means 110, for example, can obtain a plurality of supervisorydata from a storage means that may be connected to the system 100,network, system for generating a pain estimation model, apparatusgenerating a pain classifier, or system generating a model fordifferentiation of the pain of an object. The obtained plurality ofsupervisory data is passed on to the processor 120.

In step S302, the first processing means 121 of the processor 120derives a covariance matrix from the plurality of supervisory dataobtained in step S301. Each of the plurality of supervisory dataobtained by the obtaining means 110 has a plurality of features, whereinthe plurality of supervisory data may be expressed with an n×d matrix. nis the number of supervisory data (number of samples) and d is thenumber of features included in each supervisory data. The firstprocessing means 121 may derive a covariance matrix from the n×d matrix.In the derived covariance matrix, the diagonal component retainsvariance of each feature and the other components retain covariancebetween two features.

The derived covariance matrix is passed on to the second processingmeans 122 of the processor 120.

In step S303, the second processing means 122 of the processor 120decomposes the covariance matrix derived in step S302. The secondprocessing means 122, for example, can decompose a covariance matrixinto Q*Q′, wherein the matrix Q′ is a transposed matrix of matrix Q.This decomposition may be any of, for example, Cholesky decomposition,LU decomposition, QR decomposition and singular value decomposition.When the decomposition is Cholesky decomposition, LU decomposition, orQR decomposition, the matrix Q or matrix Q′ would be an upper triangularmatrix as a result. The processing of when the decomposition is Choleskydecomposition, LU decomposition, or QR decomposition, is discussed indetail while referring to FIG. 4.

Alternatively, the second processing means 122 can, for example,decompose a covariance matrix into V*L*V′, wherein matrix L is adiagonal matrix consisting of an eigenvalue, matrix V is a matrix havinga corresponding right eigenvector vector in the column and matrix V′ isa transposed matrix of matrix V. This corresponds to the act of carryingout the principal component analysis to a plurality of supervisory data.The processing of carrying out principal component analysis to aplurality of supervisory data is discussed in detail while referring toFIG. 5A and FIG. 5B.

The matrix of the result that has been decomposed is passed on to thethird processing means 123 of the processor 120.

In this step S304, the third processor means 123 of the processor 120applies a random number to a decomposed matrix. The third processingmeans 123 can apply a random number by multiplying the decomposed matrixby a random number matrix. The random number may be, for example, anormal random number. The third processing means 123 can, for example,apply a random number to the decomposed matrix Q or matrix Q′. Forexample, when the second processing method 122 carried out Choleskydecomposition, a random number can be applied to matrix Q′ (uppertriangular matrix) of the result that has been decomposed by Choleskydecomposition. For example, when the second processing means 122 carriedout Cholesky decomposition, a random number can be applied to matrix Q(lower triangular matrix) of the result that has been decomposed byCholesky decomposition. For example, when the second processing means122 carried out LU decomposition, a random number can be applied tomatrix Q′ (upper triangular matrix) of the result that has beendecomposed by the LU decomposition. For example, when the secondprocessing means 122 carried out QR decomposition, a random number canbe applied to matrix Q′ (upper triangular matrix) of the result that hasbeen decomposed by the QR decomposition. For example, when the secondprocessing means 122 carried out singular value decomposition, a randomnumber can be applied to matrix (U*W_)′ of the result that has beendecomposed by the singular value decomposition. For example, a randomnumber can be applied to matrix sqrt(L) of the result that has beendecomposed into V*L*V′. In this regard, sqrt( ) is a function employinga square root.

The number of the rows can be increased while retaining the relationshipof features of a plurality of supervisory data in the matrix obtained asa result by multiplication by a random number having the number of rowsthat is wished to be reached. Since the rows correspond to the number ofsupervisory data (number of samples), it can be considered that thenumber of samples is augmented in the matrix obtained as a result.

Since the above-mentioned processing enables increase of the number ofsamples while retaining the relationship of features of a plurality ofsupervisory data, the prediction precision does not decrease even whenmachine learning is carried out using augmented data. In other words,the augmented data can be used as supervisory data significant inmachine learning. This is especially useful when learning data obtainedfrom an organism. This is because that the number of times of directlyobtaining data from an organism can be decreased extremely. For example,when reaction data against stimulation is obtained, it is possible toobtain supervisory data in the amount that enables application tomachine learning for predicting pain only by imposing stimulation on theorganism several times. This can reduce burden on the organism.

FIG. 4 shows an example of a processing 400 in the system 100 foraugmenting supervisory data used for machine learning. The processing400 is a processing of when the second processing means 122 of theprocessor 120 decomposes a covariance matrix by one of Choleskydecomposition, LU decomposition and QR decomposition.

In step S401, the obtaining means 110 of the processor 120 obtains aplurality of supervisory data. Step S401 may be the same processing asthe above-described step S301.

In step S402, the first processing means 121 of the processor 120calculates the mean value for every feature from a plurality ofsupervisory data obtained in step S401. The mean value for every featuremay be calculated by, for example, averaging the value of each column ofthe n×d matrix of a plurality of supervisory data.

In step S403, the first processing means 121 of the processor 120derives a covariance matrix from the plurality of supervisory dataobtained in step S301. Step S403 may be the same processing as theabove-described step S302.

In step S404, the second processing means 122 of the processor 120decomposes the covariance matrix derived from step S403. Specifically,the second processing means 122 of the processor 120 carries out one ofCholesky decomposition, LU decomposition and QR decomposition to thecovariance matrix to derive an upper triangular matrix. For example,when a covariance matrix is decomposed into Q*Q′ by Choleskydecomposition, matrix Q would become an upper triangular matrix. Forexample, when a covariance matrix is decomposed into Q*Q′ by LUdecomposition, matrix Q′ would become an upper triangular matrix. Forexample, when a covariance matrix is decomposed into Q*Q′ by QRdecomposition, matrix Q′ would become an upper triangular matrix.

In step S405, the third processing means 123 of the processor 120applies a random number to the upper triangular matrix derived in stepS404. The third processing means 123 can apply a random number, by forexample, multiplying the upper triangular matrix by a normal randomnumber matrix.

In step S406, the third processing means 123 of the processor 120 adds amean value to each feature of the upper triangular matrix to which arandom number has been applied in step S405. This enables recovery ofinformation originally held by a plurality of supervisory data deletedby forming a covariance matrix of the plurality of supervisory data.

Since the above-described processing enables increase in the number ofsamples while retaining the relationship of features of a plurality ofsupervisory data, the prediction precision does not decrease even whenmachine learning is carried out using augmented data. In addition,decomposition of a covariance matrix by Cholesky decomposition not onlyenables augmentation of a sample by providing a random number to adecomposed upper triangular matrix, but also enables sample augmentationby applying a random number to a decomposed lower triangular matrix.This achieves the advantage of being able to augment more samples.

FIG. 5A shows an example of a processing 500 in the system 100 foraugmenting supervisory data used for machine learning. The processing500 is a processing of when the second processing means 122 of theprocessor 120 carries out principal component analysis.

In step S501, the obtaining means 110 of the processor 120 obtains aplurality of supervisory data. Step S501 is the same processing as theabove-described step S301.

In step S502, the first processing means 121 of the processor 120calculates a mean value for every feature from the plurality ofsupervisory data obtained in step S501. The mean value for every featuremay be calculated by, for example, averaging the value of each column ofthe n×d matrix of a plurality of supervisory data.

In step S503, the first processing means 121 of the processor subtractsthe mean value for every feature of the plurality of supervisory dataobtained in step S502 from each feature of the plurality of supervisorydata. The mean value for every feature may be, for example, subtractedfrom the value of the column of the n×d matrix of a plurality ofsupervisory data.

In step S504, the second processing means 121 of the processor 120calculates a principal component space by carrying out principalcomponent analysis to the plurality of supervisory data in which themean value has been subtracted in step S503.

In step S505, the second processing means 122 of the processor 120calculates the principal component score by projecting the plurality ofsupervisory data in which the mean value has been subtracted in stepS503 to the principal component space calculated in step S504.

In step S506, the second processing means 122 of the processor 120calculates the standard deviation based on the principal component scorecalculated in step S505.

In step S507, the third processing means 123 of the processor 120applies a random number to the standard deviation calculated in stepS506. The random number is, for example, a normal random number.

In step S508, the fourth processing means 124 of the processor 120carries out projection conversion of the standard deviation to which arandom number has been applied in step S507 and adds the mean valuecalculated in step S502 to the data that underwent projectionconversion. This enables recovery of information originally held by theplurality of supervisory data deleted in the process of subtracting themean value.

Since the above-described processing enables increase in the number ofsamples while retaining the relationship of features of a plurality ofsupervisory data, the prediction precision does not decrease even whenmachine learning is carried out using augmented data.

FIG. 5B shows a processing 510 which is equivalent to the processing500. The processing 510 is a processing comprising derivation of acovariance matrix from a plurality of supervisory data and decompositionof the covariance matrix into V*L*V′.

In step S511, the obtaining means 110 of the processor 120 obtains aplurality of supervisory data. Step S511 is the same processing as theabove-described step S501.

In step S512, the first processing means 121 of the processor 120calculates a mean value for every feature from the plurality ofsupervisory data obtained in step S511. The mean value for every featuremay be calculated by, for example, averaging the value of each column ofthe n×d matrix of a plurality of supervisory data. Step S512 is the sameprocessing as the above-described step S502.

In step S513, the first processing means 121 of the processor 120subtracts the mean value of every feature of the plurality ofsupervisory data obtained in step S512 from each feature of theplurality of supervisory data. The mean value of every feature may besubtracted from, for example, the value of the column corresponding tothe n×d matrix of a plurality of supervisory data. Step S513 is the sameprocessing as the above-described step S503.

In step S514, the first processing means 121 of the processor 120derives a covariance matrix from the plurality of supervisory data inwhich a mean value has been subtracted in step S513. Step S514 may bethe same process as the above-described step S302.

In step S515, the second processing means 122 of the processor 120decomposes the covariance matrix derived in step S514 into V*L*V′. Inthis regard, matrix L is a diagonal matrix consisting of an eigenvalue,matrix V is a matrix having a corresponding right eigenvector vector inthe column and matrix V′ is a transposed matrix of matrix V.

In step S516, the third processing means 123 of the processor 120applies a random number to the sqrt(L)=M of the L derived in step S515.In this regard, sqrt( ) is a function employing a square root. The thirdprocessing means 123 can apply a random number by multiplying M by arandom number matrix.

In step S517, the fourth processing means 124 of the processor 120multiplies M to which a random number has been applied in step S516 bymatrix V′ and adds the mean value calculated in step S512. This enablesrecovery of information originally held by the plurality of data deletedin the process of forming a covariance matrix.

Since the above-described processing enables increase in the number ofsamples while retaining the relationship of features of a plurality ofsupervisory data, the prediction precision does not decrease even whenmachine learning is carried out using augmented data.

Furthermore, the principal component analysis can be carried out onlywhen the number of supervisory data (number of samples) n obtained bythe obtaining means 110 and the number of features d included in eachsupervisory data would be n>d. When n<d, there is a need to add noise(e.g., 1/10 of the standard deviation or the like) to increase thenumber of data until n>d is achieved. In this regard, since theprincipal component analysis can also be carried out in the case of n<dwhen a covariance matrix is decomposed by Cholesky decomposition, LUdecomposition, QR decomposition, or singular value analysis, thepre-processing of increasing data before decomposition is not required.

The above-described processing has been explained to be carried out in aspecific order, but the present disclosure is not limited thereto. Theabove-described processing can be carried out in any logically possibleorder.

The above-described processing, for example, divides the supervisorydata obtained by the obtaining means 110 into a plurality of subunits,wherein each processing may be caused to be carried out to each of theplurality of divided subunits. Upon doing so, sample augmentation can becompleted by summing the total of the subunits that have been augmented.

The system 100 for augmenting supervisory data used for machine learningdescribed above can be used, for example, for augmenting the dataobtained by an organism in machine learning of the data obtained fromthe organism. The data obtained from the organism may be, for example,brainwave data, MRI image data, or gene expression data, and may be, forexample, brainwave data or MRI data of when pain is applied to theorganism.

An embodiment of estimating pain from brainwave data of when pain isapplied to an organism is explained below.

First, the relationship between pain due to electrical stimulation andbrainwaves is described. The data provided hereinafter shows data forone representative subject from a plurality of subjects.

FIG. 6A is a graph showing the relationship between electricalstimulation and pain level (VAS). FIG. 6B is a graph showing therelationship between electrical stimulation and pain level (pairedcomparison). FIG. 1C is a graph showing the relationship betweenelectrical stimulation and brainwave amplitude. FIG. 6D is a graphshowing an example of a waveform of a brainwave.

The horizontal axes of FIGS. 6A, 6B, and 6C indicate the value ofcurrent of electrical stimulation. The vertical axis of FIG. 6Aindicates the pain level reported by the subject in accordance with VAS.The vertical axis of FIG. 6B indicates the pain level reported by thesubject in accordance with paired comparison. The vertical axis of FIG.6C indicates the value of brainwave amplitude. In FIG. 6D, thehorizontal axis indicates time, and the vertical axis indicates signallevels.

Paired comparison is a method of using two magnitudes of electricalstimulation as a set and having a subject report which electricalstimulation is how much more painful by a numerical value for each of aplurality of sets of electrical stimulation. In such a case, pain levelsare reported by comparing two pains, so that the effect of pastexperience of a subject with respect to pain levels can be mitigated.

As shown in FIGS. 6A and 6B, the relationship between the value ofcurrent of electrical stimulation (i.e., intensity of stimulation) andpain level is represented roughly by a sigmoid (S-shaped) curve,regardless of which of VAS or paired comparison method is used. Theshape of the sigmoid curve (e.g., the upper limit value and lower limitvalue, and the like) varies depending on the subject.

As shown in FIG. 6C, the relationship between the value of current ofelectrical stimulation and the value of brainwave amplitude is alsoroughly represented by a sigmoid curve. In this regard, the differencebetween the maximum peak value and minimum peak value (i.e.,peak-to-peak value) is used as the value of brainwave amplitude. Forexample in FIG. 6D, the maximum value of difference (N1−P1) among threedifferences (N1−P1, N2−P2, and N1−P2) is used as the value of amplitude.

In this manner, the relationship between the intensity of electricalstimulation and pain level and the relationship between the intensity ofelectrical stimulation and the value of brainwave amplitude are bothrepresented by a sigmoid curve. In other words, pain levels andbrainwave amplitude both have an upper limit and lower limit toelectrical stimulation and exhibit a similar change with respect to theintensity of electrical stimulation. In this regard, the relationshipbetween the value of brainwave amplitude and pain level, when analyzed,was represented as shown in FIGS. 6E and 6F.

FIG. 6E is a graph showing the relationship between pain level due toelectrical stimulation (VAS) and brainwave amplitude. FIG. 6F is a graphshowing the relationship between pain level due to electricalstimulation (paired comparison) and brainwave amplitude. In FIGS. 6E and6F, the horizontal axis indicates the brainwave amplitude, and thevertical axis indicates the pain level.

As shown in FIGS. 6E and 6F, the pain level due to electricalstimulation and the value of brainwave amplitude have linearity for bothVAS and paired comparison. In other words, the value of brainwaveamplitude is proportional to the pain level.

As used herein, linearity includes strict linearity as well assubstantial linearity. In other words, linearity includes relationshipsthat can be approximated to linearity within a given range of error. Agiven range of error is defined, for example, by a coefficient ofdetermination R² in regression analysis. The coefficient ofdetermination R² is a value found by subtracting 1 from a result ofdividing the Residual Sum of Squares by total Sum of Squares of thedifference in the observed value from the mean value. The give range oferror is, for example, a range where R² is 0.5 or greater.

For the relationship between pain due to thermal stimulation andbrainwaves, the pain level and brainwave amplitude also have linearityin the same manner as electrical stimulation.

FIG. 6G is a graph showing the relationship between the pain level dueto thermal stimulation (VAS) and brainwave amplitude. FIG. 6H is a graphshowing the relationship between the pain level due to thermalstimulation (paired comparison) and brainwave amplitude. In FIGS. 6G and6H, the horizontal axis indicates the brainwave amplitude, and thevertical axis indicates the pain level.

As shown in FIGS. 6G and 6H, the pain level due to thermal stimulationand the value of brainwave amplitude have linearity for both VAS andpaired comparison. While the upper limit value and lower limit value ofthe value of brainwave amplitude have variations depending on thesubject, the inventors found through previous experiments that the upperlimit value of amplitude does not exceed about 60 μV.

In this manner, this embodiment has elucidated that brainwave amplitudesand pain have a specific relationship as a result of analyzing therelationship between values of brainwave amplitude and pain levels fromevaluation of a plurality of types of pain by a plurality of methods. Inaddition, the present disclosure found that a pain classifier forestimating the magnitude of pain can be calculated based on the specificrelationship between brainwave amplitudes and pain.

Upon doing so, the methodology of the sample augmentation describedabove can be incorporated.

A pain level differentiation/estimation apparatus 1110 for monitoringpain of an object being estimated based on a brainwave of the objectbeing estimated of the present disclosure comprises: A) a brainwave datameasurement unit 1111 for obtaining brainwave data or analysis datathereof of the object being estimated; B) a feature extraction unit 1112for retrieving feature based on the brainwave data and analysis datathereof (e.g., electrical potential, frequency, others, a mean value(e.g., arithmetic/geometric mean potential) to obtain a temporal changeof a mean value (e.g., arithmetic/geometric mean potential) in aspecific time frame), and optionally a pain index generation unit 1113,and optionally a pain monitoring standard determination unit 1114 fordetermining a baseline of monitoring using a pain index (e.g., degree offeature indicating a strong level of pain or the like) forchronologically evaluating or monitoring a level of pain of the objectbeing estimated from the brainwave data; and C) a pain level monitoringunit 1115 for monitoring a level of pain of the object being estimatedfrom the brainwave data or analysis data thereof. The brainwave datameasurement unit 1111 performs step S100 of FIG. 9, the featureextraction unit 1112 performs step S200, the pain index generation unit1113 optionally generates a pain index, a standard determination unitoptionally determines a standard (step S300), and the pain levelmonitoring unit 1115 performs step S400. The pain level monitor unit1115 may be caused to, for example, monitor the level of pain based onthe temporal change of the mean value of the feature. The featureextraction unit and pain level monitor unit and optional pain indexgeneration unit and standard determination unit may be configured sothat the same part has three functions, or configured as separate parts.A pain index generation unit and standard determination unit areoptional parts, so that a pain index and/or a standard value (baseline)can be introduced or received from an external source. Sampleaugmentation can be applied to the data obtained by the brainwave datameasurement unit 1111. The sample augmentation may be applied in thebrainwave data measurement unit 1111, or may be applied in a differentconfiguration unit such as the feature extraction unit 1112, the painindex generation unit 1113, or the pain monitoring standarddetermination unit 1114, or a sample augmentation unit 1116 may beprovided independently as shown in FIG. 10. The sample augmentation unit1116 performs, for example step S250. Sample augmentation is efficientand preferred to be carried out after completion of the featureextraction processing by the feature extraction unit 1112.

FIG. 10 is a block diagram showing the functional configuration of thesystem 1100 comprising a pain level differentiation/estimation apparatusof one embodiment. The system 1100 comprises the pain leveldifferentiation/estimation apparatus 1110 and an electroencephalograph1120. An electroencephalograph can be configured to be separate from thepain level differentiation/estimation apparatus in this manner.

The apparatus 1110 comprises the measurement unit 1111, the featureextraction unit 1112, the pain index generation unit 1113, the standarddetermination unit 1114, and the pain level monitoring unit 1115. Theapparatus 1110 is materialized by, for example, a computer comprising aprocessor and a memory. In such a case, the apparatus 1110 makes theprocessor function as the measurement unit 1111 and the featureextraction unit 1112, and optionally the pain index generation unit 1113and the standard determination unit 1114 when a program stored in thememory is implemented by the processor. The apparatus 1110 can also bematerialized by, for example, a dedicated electrical circuit. Adedicated electrical circuit can be a single integrated circuit or aplurality of electrical circuits. The amplitude data measurement unit1111 and the pain index generation unit 1113 for providing a pain indexto the standard determination unit can be configured internally orexternally.

Sample augmentation can be applied to data obtained at the measurementunit. Sample augmentation may be applied in the measurement unit 1111,or may be applied at a different configuration such as the featureextraction unit 1112, the pain index generation unit 1113, or thestandard determination unit 1114, or a sample augmentation unit 1116 maybe independently provided.

The measurement unit 1111 obtains a plurality of brainwave data bymeasuring a brainwave from an object being estimated 1000 via theelectroencephalograph 1120. The object being estimated 1000 is anorganism in which a change in brainwave is induced by pain, which can bean organism having a pain sensing nerve (e.g., vertebrae such as mammalsand avian (including livestock animals, pet animals, and the like) andis not limited to humans.

The feature extraction unit 1112 generates each type of feature (e.g., amean value of brainwave data in an appropriate time frame). The unitoptionally provides a feature to the pain index generation unit 1113 andthe pain monitoring standard determination unit 1114 for a higher levelmodel based pain level differentiation/estimation (machine learning orthe like) or online monitoring of pain level. When calculating a meanvalue, the mean value can be calculated by a methodology that is wellknown in the art. An algorithm for such a calculation method can bestored in advance in the feature extraction unit 1112 or inputted uponuse via a medium or a communication function. In other words, when usinga mean value, the feature extraction unit 1112 can generate a featurefor pain trend monitoring including mean data. The pain level monitoringunit 1115 tracks a feature including calculated mean data to contributeto a supervisor monitoring or evaluating subjective pain of an object.The monitoring unit 1115 can express the output results of the standarddetermination unit 1114 as individual number (pain level of 0 to 100 orthe like) or a change in numbers.

The electroencephalograph 1120 measures the electrical activitygenerated in the brain of an object being estimated with an electrode onthe scalp. The electroencephalograph 1120 then outputs brainwave data,which is the result of measurement in concert with the measurement unit1111.

The process or method of the apparatus configured in such a manner canexecute the flow chart showing the series of processes exemplified inFIG. 9. In other words, S100 to S400 of FIG. 9 can be executed with theapparatus shown in FIG. 10.

The present disclosure can be utilized as a medical apparatus with theconfiguration exemplified in FIG. 10. For example, data is obtained froma subject undergoing pain evaluation and sample augmentation is carriedout. The mean is calculated as needed. The method of calculating themean value in such a case can be performed while monitoring the data orperformed after accumulation of data to a certain degree, or both.Herein, after the sample is augmented, a mean value or another featureis calculated, and the point of time where the pain level is strong isidentified based on a pain index, so that a baseline of pain trend canbe determined. Furthermore, after calculating a mean value or anotherfeature, sample augmentation is carried out and the point of time wherethe pain level is strong is identified based on a pain index, so that abaseline of pain trend can be determined. It is more efficient andpreferred that sample augmentation is carried out after calculating thefeature. In other words, brainwave data (or analysis data thereof) isobtained from the electroencephalograph 1120 and measured at themeasurement unit 1111. A feature or the like is extracted from themeasurement value at the feature extraction unit 1112. Sampleaugmentation can be carried out after obtainment or measurement orbrainwave data or analysis data thereof, or after extraction of feature.Optionally, a pain index can be generated at the pain index generationunit 1113 from the feature, and a baseline to be used as the standardfor pain level monitoring can be determined using the index at thestandard determination unit 1114. With a specific embodiment using ahierarchical pain trend monitoring methodology, when a model is alreadydetermined, a trend of pain levels can be monitored online as soon as abaseline is obtained or determined.

OTHER EMBODIMENTS

The pain estimation apparatus according to one or more embodiments ofthe present disclosure has been described based on the embodiments, butthe present disclosure is not limited to such embodiments. Variousmodifications applied to the present embodiments and embodimentsconstructed by combining constituent elements in different embodimentsthat are conceivable to those skilled in the art are also encompassedwithin the scope of one or more embodiments of the present disclosure aslong as such embodiments do not deviate from the intent of the presentdisclosure.

Some or all of the constituent elements of the pain estimation apparatusin each of the above embodiments can be comprised of a single system LSI(Large Scale Integration). For example, the apparatus 1110 can becomprised of system LSI having the measurement unit 1111, mean dataobtaining unit 1112, the pain monitoring standard determination unit1114 and pain level monitoring unit 1115.

System LSI is ultra-multifunctional LSI manufactured by integrating aplurality of constituents on a single chip, or specifically a computersystem comprised of a microprocessor, ROM (Read Only Memory), RAM(Random Access Memory), and the like.

A computer program is stored in a ROM. The system LSI accomplishes itsfunction by the microprocessor operating in accordance with the computerprogram.

The term system LSI is used herein, but the term IC, LSI, super LSI, andultra LSI can also be used depending on the difference in the degree ofintegration. The methodology for forming an integrated circuit is notlimited to LSI. An integrated circuit can be materialized with adedicated circuit or universal processor. After the manufacture of LSI,a programmable FPGA (Field Programmable Gate Array) or reconfigurableprocessor which allows reconfiguration of the connection or setting ofcircuit cells inside the LSI can be utilized.

If a technology of integrated circuits that replaces LSI by advances insemiconductor technologies or other derivative technologies becomesavailable, functional blocks can obviously be integrated using suchtechnologies. Application of biotechnology or the like is also apossibility.

One embodiment of the present disclosure can be not only such a painindex generation, pain differentiation/classification apparatus, butalso a pain index generation, pain level monitoring method usingcharacteristic constituent units in the pain differentiation/estimationapparatus as steps. Further, one embodiment of the present disclosurecan be a computer program for implementing each characteristic step inthe pain index generation, pain level monitoring method on a computer.Further, one embodiment of the present disclosure can be a computerreadable non-transient recording medium on which such a computer programis recorded.

In each of the embodiments described above, each constituent element canbe comprised of a dedicated hardware or materialized by implementingsoftware program that is suited to each constituent element. Eachconstituent element can be materialized by a program implementation unitsuch as a CPU or a processor reading out and implementing a softwareprogram recorded on a recording medium such as a hard disk orsemiconductor memory. In this regard, software materializing the paindifferentiation/estimation apparatus of each of the embodimentsdescribed above is a program such as those described below.

Specifically, this program makes a computer implement a method ofmonitoring pain of an object being estimated based on a brainwave of theobject being estimated, comprising: a) measuring or obtaining brainwavedata or analysis data thereof by measuring brainwaves in response tostimulation from the object being estimated, b) obtaining a temporalchange of a mean value (e.g., arithmetic/geometric mean potential) ofthe brainwave data or analysis data thereof in a specific time frame(and optionally extracting a feature for temporal change in the meanvalue), and optionally generating a pain index (e.g., degree of featureindicating a strong pain level) for chronologically evaluating ormonitoring a level of pain of the object being estimated from brainwavedata and optionally determining a baseline of monitoring, and c)optionally monitoring a level of pain of the object being estimated fromthe brainwave data based on the baseline determination process.

The present disclosure provides a recording medium storing a program forimplementing a method of monitoring pain of an object being estimatedbased on a brainwave of the object being estimated on a computer. Theprogram makes the computer implement the method of monitoring pain of anobject being estimated based on a brainwave of the object beingestimated, comprising: a) measuring or obtaining from the object beingestimated brainwave data or analysis data thereof by measuring abrainwave in response to stimulation; b) obtaining and plotting afeature based on brainwave data or analysis data thereof (e.g., atemporal change of a mean value (e.g., arithmetic/geometric meanpotential) of the brainwave data or analysis data thereof in a specifictime frame, wherein extraction of a feature for obtaining a temporalchange of the mean value when appropriate may be carried out), whereinsample augmentation of a feature based on the brainwave data or analysisdata thereof may be carried out after a) or after b), and then,optionally generating a pain index (e.g., degree of feature indicating astrong pain level) for chronologically evaluating or monitoring a levelof pain of the object being estimated from brainwave data and optionallydetermining a baseline of monitoring; and c) monitoring a level of painof the object being estimated from the brainwave data or analysis datathereof based on a change in the mean value over time.

It is understood that the method implemented herein can use one or acombination of a plurality of any of the embodiments described regardingthe system 100 for augmenting supervisory data used for machine learningof the present disclosure.

In another aspect, the present invention provides a method of generatinga model for differentiating pain of an object, comprising:

a) the step of obtaining brainwave data or analysis data thereof fromthe object;

b) the step of contracting features based on the brainwave data oranalysis data thereof with respect to the pain after determining afeature coefficient associated with the pain as needed;

c) augmenting the brainwave data or analysis date thereof, the featuresbefore contracting, or the features that have been weighted after thecontracting or combination thereof, comprising:

-   -   i) deriving a covariance matrix from brainwave data or analysis        data thereof, or the features that have been weighted after the        contracting or combination thereof;    -   ii) decomposing the covariance matrix; and    -   iii) applying a random number to the decomposed matrix;

d) the step of creating a differentiation analysis model by machinelearning and examination based on the features that have been weightedafter the contracting or combination thereof; and

e) the step of determining a differentiation analysis model achieving apredetermined precision.

Furthermore, in another aspect, the present disclosure provides a methodof generating a model for differentiating pain of an object, comprising:

a) the step of obtaining brainwave data or analysis data thereof fromthe object;

b) the step of extracting features based on the brainwave data oranalysis data thereof;

c) augmenting the features, comprising:

-   -   i) deriving a covariance matrix from the features;    -   ii) decomposing the covariance matrix; and    -   iii) applying a random number to the decomposed matrix;

d) the step of determining a feature coefficient from the augmentedfeatures to carry out contraction with respect to the pain as needed;

e) the step of creating a differentiation analysis model by machinelearning and examination based on the features that have been weightedafter the contracting or combination thereof; and

f) the step of determining a differentiation analysis model achieving apredetermined precision.

In one embodiment, the present disclosure is characterized by performingthe contraction, after determining the feature coefficient, by repeatingdifferentiation and estimation, calculating a mean of and ranking thefeature coefficients for the differentiation and estimation, andselecting a feature based on a given threshold value in step b). Thefeature coefficients are determined by, preferably but not limited to,machine learning.

Examples of machine learning include Support Vector Machine (SVM),neutral network, deep learning, logistic regression, reinforcementlearning, and the like.

In another embodiment, the present disclosure comprises, upondetermining the feature coefficients in b), determining a hyperparameterresulting in the highest differentiation accuracy, and determining thefeature coefficients based on the hyperparameter, and excluding afeature which has no effect or a low ratio of contribution fordifferentiation. The feature coefficients and the hyperparameter aredetermined by, preferably but not limited to, machine learning.

In another embodiment of the present disclosure, b) to e) comprise: (C1)dividing the features and data corresponding to the pain correspondingto the features into data for learning and data for testing; (C2)performing machine learning using the learning data to create adifferentiation model and calculating an optimal λ value (and a partialregression coefficient, a regression equation, and a model intercept);(C3) calculating differentiation accuracy of the differentiation modelby using the data for testing; (C4) if there is a target sample with thedifferentiation accuracy at or below a chance level in the objects,repeating steps C1 to C3 after excluding the sample, and if there is nosample at or below a chance level, ending the steps to determine adifferentiation model, wherein the chance level is a numerical valueobtained by dividing 100% by the number of classifications. In thisregard, the chancel level refers to a value obtained by dividing 100% bythe number of classifications. This is verification to avoid accidentalmatch. Such a backward elimination method is also useful for generationof a differentiation model in the present disclosure. The sampleaugmentation of the present disclosure can also be applied upon makingdata for learning.

A model created based on the method of the present disclosure, albeitjust one example, can materialize 70% accuracy even with few features,such that the significance thereof is high. There are several method ofranking and selecting features other than the methodologies of thepresent disclosure. For example, machine learning (SVM-RFE) disclosed asa comparative example is a more complex method of actually creating adifferentiation model using features while calculating and ranking aweighting coefficient of each feature.

Unlike such a method, one of the feature of the present disclosure is infirst finding the robustness of a change pattern of brainwave featuresbefore differentiating and analyzing by contracting (e.g., using theneuron firing principle, the “all-or-none” law, as the selectioncriteria) based on the most basic property of classification instead ofranking features by differentiation.

With regard to contracting before or after, it is more advantageous toperform the contracting of the present disclosure first. As a reasonthereof, the present disclosure is characterized by fitting features bycontracting (e.g., sigmoid function) and then extracting an all-or-nonefeature, and determining select few differentiation models by machinelearning, such that calculation can be simple. Meanwhile, if fittingsuch as sigmoid fitting is performed after, sigmoid would be used toidentify how many all-or-none patterns the features used in adifferentiation pattern have (or approximate) rather than used forcontracting. In such a case, machine learning is performed, individualfeature or a plurality of features are used to determine the feature ordifferentiation model with the highest accuracy, a sigmoid function isfitted to the feature with high differentiation accuracy, the robustnessof the feature is found, and if it is desirable to make an economicaldifferentiation model, only features with a high degree of fit areselected to re-run machine learning. Thus, the calculation cost would behigh and the learning process would be inefficient. In this manner, ifsigmoid fitting of features or the like additively materialize previousto “machine learning process with contracting of the number offeatures”, an addition procedure for futilely performing machinelearning would be required so that the calculation cost would be high.In view of the above, it is more advantageous to perform the contractingof the present disclosure first. In addition, in the case of anembodiment carrying out contraction, sample augmentation is moreefficient when carried out after the contraction, and is thusadvantageous. By carrying out sample augmentation right before carryingout machine learning, the machine learning can be carried out in a statein which there are enough samples.

For example, sample augmentation can be carried out before thecontraction. In this case, sample augmentation may be carried out forthe purpose of how high the precision should be upon carrying out thecontraction. In this case, the more the sample is augmented, the higherthe calculation cost for the contraction may be.

For example, sample augmentation and contraction may be carried outalternately. For example, a sample is carried out before thecontraction, wherein if the sample is not enough, sample augmentationcan be additionally carried out again after the contraction.

For contracting, for example correlation between features can be studiedto consolidate those with high correlation, or a primary factor can befound by deleting one of the features or performing factor analysis orthe like (e.g., 10 factors from 100 data or the like). Contracting canalso be materialized as in the above example by using a sigmoid functionor the like to find only features that are in accordance with a specificreaction pattern model and use the features in the model. While variousspecific patterns can be set, differentiation of “0 or 1” such as havingpain or no pain can be used for pain. In this regard, a sigmoid function(example of logistic regression) or step function can be used for 0or 1. A sigmoid function is created with “0, 1” and approximated. If astatistically significant approximation is observed, this can be used ina model. “All-or-none”, in other words, can be expressed as reacting attwo values of “0 or 1” to specific stimulation, which can be considereda feature indicating a digital discrete reaction. When contracting,contracting can be kept within a certain range by determining the targetobjective. For example, contracting can be expressed as “contractingwith respect to pain” or “contracting with respect to pain stimulation”.

The example described above is function approximation for the purpose ofcontracting features. Meanwhile, for identification of an optimaldifferentiation model, a change in differentiation accuracy of aplurality of differentiation models (from a model with few features to amodel with the maximum number of features) can be approximated to afunction to select an economical model. For example, the sigmoidfunction materializing binomial classification described above can beused for function approximation. A sigmoid function is an elementalfunction used at various levels, which can certainly be a neuron firingprinciple, as well a pain reaction function, pain differentiator, orpain occurrence function (see FIG. 13), a feature contracting tooldescribed above (see FIG. 14), or can be related to the selectionprocess of a differentiation model described herein. Therefore, in apreferred embodiment, an asymptote of the minimum value and maximumvalue is derived by sigmoid approximation by first limiting theinflection area to that with a relatively large inflection range(amplitude). Next, the variation in differentiation accuracy isexpressed as a representative value (minimum value, maximum value), andthe value of improvement in differentiation accuracy (maximumvalue−minimum value) is calculated, and with the maximum value as thethreshold value, the number of features that first exceeds this valuecan be presented as an economical differentiation model attaining themaximum gain in the percentage of improvement.

The step of differentiating and analyzing by machine learning and crossvalidation from top of ranking of weighting coefficients (includingapproximation coefficients; e.g., regression coefficients) of eachfeature after the contracting or combination thereof can differentiateand analyze by creating a ranking of features after contracting (orcombination thereof) and weighting coefficients and performing crossvalidation thereon by machine learning or the like.

In one embodiment, the present disclosure further comprises, after stepc), calculating a value of difference (Diff) of adjacent models indifferentiation accuracy obtained by differentiating and analyzing,wherein the adjacent models are models comprising n−1 features and nfeatures, wherein n is 2 or greater, and wherein judgment of adifferentiation model in step d) takes into consideration thedifference. In this regard, the value of difference is implemented insoftware such as the MATLAB function Diff or the like as is well knownin the art. This is a function for “continuously subtracting adjacentnumerical values”. In other words, the Diff function is one of thefunctions implemented in MATLAB for finding the value of difference ofadjacent values while shifting by one point. This functionmathematically corresponds to “differentiation” and mechanicallycorresponds to “speed”. While the usage varies, for signal processing,this can be used to find a rapid or discontinuous (or stepwise)inflection point in a time series or spatial data distribution. Forsigmoid functions, the Diff function is effective for finding aninflection area. The function can also be used to find a temporal orspatial point where the feature dramatically changes in brainwaves.

In one embodiment, taking into consideration of the value of differencecomprises a process of re-ranking features from values with a greatervalue of difference and recalculating differentiation accuracy togenerate a model with higher differentiation accuracy.

In one embodiment, the judgment based on the value of differencecomprises classifying the features into a main feature and supporterfeatures and re-ranking the supporter features. In this regard, afeature with a number one ranking or a feature with significantcorrelation thereto can be selected as the main feature and others canbe selected as supporter features for re-ranking based on a value ofdifference of n feature model and n−1 feature model when supporterfeatures are inputted. For example, the function Diff implemented inMATLAB or the like can be used. For example, the number 1 ranking can befixed and the rest can be used as supporters. Therefore, the single mainfeature, or the combination of the main feature and other supporterfeatures can be used.

As used herein, the number 1 ranking feature after rearranging by R²values is referred to as the single main feature. Since top rankingfeatures exhibiting significant correlation with the single main featurehave a similar differentiation pattern, such a group of features that isnot limited to one single main feature can also be collectively the mainfeature. In such a case, number 1 ranked feature and features thatexhibit significant correlation thereto can be the main feature.

In one embodiment, the method of the present disclosure comprises, afterthe re-ranking, changing the features and repeating step c).

In another embodiment of the present disclosure, the method comprises,after the re-ranking, changing the features and performing machinelearning (linear, logistic regression, support vector machine, or thelike) and cross validation to calculate differentiation accuracy of eachmodel. After re-ranking, a feature is increased one at a time to performmachine learning (linear, logistic regression, support vector machine,or the like) and cross validation to calculate differentiation accuracyof each model. The most accurate model can be selected thereby. In thepresent disclosure, any machine learning can be used. Linear, logistic,support vector machine (SVM) or the like can be used as supervisedmachine learning. The sample augmentation methodology of the presentdisclosure can also be provided in an aspect of carrying out suchmachine learning.

In one embodiment, the step described above can be repeated at leastonce. In this regard, the ranking of features has changed, so that onlythe inputted feature would be different. The sample augmentation of thepresent disclosure can be re-applied upon repetition.

Once model candidates are calculated, a model attaining a given accuracycan be optionally determined to generate a model of interest.

A given accuracy can be appropriately determined in accordance with theobjective. A model with the highest accuracy and fewest number offeatures, a model with the highest accuracy, or the like can bedetermined. For example, if there are a plurality of differentiationmodels that attain the same or same degree of differentiation accuracy,it is preferable to select a model comprising the fewest number offeatures (this is referred to as the economical standard).

Thus, one embodiment comprises selecting a model with fewer types offeatures among models that attain a give accuracy.

In another embodiment, the given differentiation accuracy comprises thehighest accuracy. Contracting can be characterized by extracting aneffective feature. More specifically, contracting is characterized byselecting a feature close to all or none corresponding to having pain orno pain, i.e., two value feature. To extract a feature associated with asubordinate classification within having pain or no pain, the sameprocess can be repeated to further recursively perform contracting.

In one embodiment, the weighting coefficient is used in the same meaningas an approximation index of a differentiation function, and is selectedfrom the group consisting of an R² value, a correlation coefficient, aregression coefficient, a residual sum of squares (difference betweendifferentiation function and feature), and the like.

In another embodiment, a differentiation function (model) is selectedfrom the group consisting of a sigmoid function, step function, and thelike.

In still another embodiment, a model approximation index is a subset ofweighting coefficients, and is selected from the group consisting of anR² value, a correlation coefficient, a regression coefficient, aresidual sum of squares, and a subset thereof.

In still another embodiment, the effective feature, for binomialclassification, is presence or absence corresponding to having pain orno pain, i.e., a 2 value feature, or a feature with higher approximationto a differentiation function.

FIG. 15 describes a schematic diagram of the apparatus of the presentdisclosure (differentiation apparatus with a process of contractingfeatures) (101000 to 108000) (it should be noted that some of theconfiguration diagrams are optional constituents that can be omitted).In this schematic diagram, each step is described when appropriate(S10100 to S10600).

This apparatus is comprised of a feature contracting unit 101000,feature extraction unit 102000, pain differentiation/estimation modelgeneration unit 103000, pain differentiation/estimation unit (cancomprise a model correction process) 104000, reference stimulationapplication unit 105000, brainwave data measurement unit 105200, andpain level visualization unit 107000. An object is denoted as 106000.

In such differentiation with a process of contracting, the number ofpain differentiation/estimation stages (2 stages, 3 stages, or the like)is determined (S10100), and a differentiation function is generated(examples include sigmoid functions in binomial classification and thelike; S10200). A feature is obtained after a reference stimulation(electrical stimulation or the like) is applied to the object 106000from the reference stimulation application unit 105000 in accordancewith the differentiation stage determined at S10100, and a featurerelated to a pain stage is collected (S10300) and contracted (S10400).Sample augmentation may be carried out after collecting feature, or maybe carried out after contraction. The collected feature is approximatedby a differentiation function generated at S10200 and ranked inaccordance with the magnitude of the obtained approximation coefficient(regression coefficient or the like). Features are used in order fromthe top features. The pain level of reference stimulation isdifferentiated and estimated with the pain differentiation/estimationunit 104000, and a differentiation model with the number of featureswith the highest differentiation accuracy is used for monitoring pain.This is one embodiment of the process of contracting features S10400.

A differentiation model (algorithm) installed in a paindifferentiation/estimation unit used in the process of contracting(white arrows) and actual pain monitoring process (black arrows) iscreated at the pain differentiation/estimation model generation unit103000, and installed in the pain differentiation/estimation unit104000. A differentiation model may be created using, for example, aplurality of supervisory data augmented by the system 100 for augmentingsupervisory data used for machine learning described above. Aftercompletion of the preprocessing described above at the featurecontracting unit 101000, actual pain related brainwave data is collectedfrom the object 106000 at the brainwave data measurement unit 105200comprising an electroencephalograph or the like (S10500). This istransmitted to the feature extraction unit 102000 and converted to afeature selected in the process of contracting amplitudes or frequencies(e.g., specific amplitude or frequency band of specific electrodes orthe like). The extracted feature is taken into the paindifferentiation/estimation unit 104000 (can comprise a model correctionprocess) from the feature extraction unit 102000, and a pain level isdetermined (S10600).

The result of the determination is indicated as a trend of changes ornumerical value (e.g., 1 to 100) at the pain level visualization unit107000.

The determination of pain differentiation/estimation stages at S10100determines the number of levels to be differentiated or estimated (e.g.,2 stages, 3 stages, or the like).

The generation of a differentiation function at S10200 creates adifferentiation function used in accordance with the number ofdifferentiation levels of S10100 (sigmoid function or step function inbinomial classification or the like).

The collection of pain stage related features at S10300 appliesreference stimulations (electrical stimulation or the like) a pluralityof times from the reference stimulation application unit 105000 to theobject 106000 in accordance with the number of levels determined atS10100 to collect related brainwave features.

In contracting of features at S10400, features obtained at S10300 areapproximated with a differentiation function, features with a highapproximation index (e.g., R² value or the like) are ranked, andfeatures are inputted into the pain differentiation/estimation unit4104000 in order from top ranking features to differentiate and estimatea level of reference stimulation. A model with a number of features withthe highest differentiation accuracy thereamong is used for actual paindifferentiation/estimation.

For collection of pain related brainwave data at S10500, actual painrelated brainwave data subjected to monitoring of pain is collectedafter completion of the contracting process at the feature contractingunit 101000. This step is data collection in an actual pain monitoringprocess. Sample augmentation may be applied to data collected by thisdata collection.

For pain level determination at S10600, actual pain related dataobtained at S10500 is processed at the feature extraction unit 102000 toobtain a feature set and then differentiated and estimated at the paindifferentiation/estimation unit 104000, a pain level is quantified froman estimated value, and a pain level is determined and made visible atthe pain level visualization unit 107000.

The apparatus 108000 is configured to comprise or to be connected to anelectroencephalograph that is or can be connected to the object(106000), so that brainwave data synchronized with stimulation emittedfrom the reference stimulation application unit 105000 to the object(106000) is obtained at the brainwave data measurement unit 105200. Thisis the summary of the apparatus 108000.

The apparatus 108000 can comprise a brainwave measurement unit, whichinternally comprises or externally connects to a brainwave recordingsensor and optionally a brainwave augmentation unit, and process signalsof a pain related brainwave and differentiate/estimate pain in theapparatus 108000. The brainwave augmentation unit may be caused toaugment the signal intensity of the brainwave and the processing of theabove-described sample augmentation may be carried out to the brainwave.

In the apparatus 108000, collected brainwave signals are processed toextract a brainwave feature at the feature extraction unit 102000. Uponextraction, a feature contracted in advance at the feature contractingunit 101000 is selected. Further, pain is (optionally) made visible atthe pain level visualization unit 107000. The apparatus internally orexternally comprises the reference stimulation application unit 105000,which applies reference stimulation such as electrical stimulation aplurality of times in accordance with the pain level determined atS10100 in order to contract features that are effective for monitoringpain of the object 106000. Brainwave data related thereto is recorded atthe brainwave data measurement unit 105200, the related brainwavefeature is obtained at the feature extraction unit 102000, a pain levelof reference stimulation is differentiated and estimated from thefeature at the pain differentiation/estimation unit 104000, and thefeature is contracted S10400 from the result thereof. The referencestimulation application unit 105000 also transmits pain stimulationinformation (stimulation type, environmental information, or the like)for differentiating an actual unknown pain level and differentiatorcreation. The reference stimulation application unit 105000 optionallycomprises a stimulation information visualization unit in addition tothe reference stimulation application unit 105000 and may displayinformation such as an image or number associated with stimulation orenvironment. The apparatus 108000 can also internally or externallycomprise the pain differentiation/estimation unit 104000 for generatinga determination value or differentiator.

In this manner, the apparatus 108000 comprises the brainwave datameasurement unit 105200 and the pain differentiation/estimation unit104000 and optionally the reference stimulation application unit 105000.The apparatus 108000 is materialized, for example, by a computercomprising a processor and a memory. In such a case, the apparatus108000 makes the processor function as the feature contracting unit101000, feature extraction unit 102000, pain differentiation/estimationmodel generation unit 103000, pain differentiation/estimation unit104000, or the like as needed when a program stored in the memory isimplemented by the processor. The processor is also made to makestimulation or environmental information visible as needed. Theapparatus 108000 of the present disclosure can be materialized, forexample, by a dedicated electronic circuit. A dedicated electroniccircuit can be a single integrated circuit or a plurality of electricalcircuits. A brainwave data obtaining unit and pleasant/unpleasantdetermination value generation unit can have the same configuration as apleasant/unpleasant determination apparatus.

The feature extraction unit 102000 can also obtain a plurality ofbrainwave data by measuring a brainwave a plurality of times from anobject being estimated via an electroencephalograph (included in thebrainwave data measurement unit 105200). An object is an organism inwhich a change in brainwave is induced by stimulation or environment,which does not need to be limited to humans. The sample augmentation ofthe present disclosure may also be applied in this stage.

The pain differentiation/estimation unit 104000 differentiates/estimatesthe degree of unpleasantness using a determination value, and alsogenerates a differentiator of determination value if not generated inadvance externally or internally. The apparatus 108000 can comprise apart generating a differentiator or determination value externally orinternally as the pain differentiation/estimation unit 104000. Adifferentiation value used for differentiation/estimation of pain is forestimating or classifying the degree of unpleasantness from amplitudesof a plurality of brainwave data. In other words, the paindifferentiation/estimation unit 104000 or the paindifferentiation/estimation model generation unit 103000 can generate adetermination value for estimating or classifying the degree ofunpleasantness of an object from brainwave data.

A brainwave recording sensor contained in the brainwave data measurementunit 105200 measures electrical activity generated inside the brain ofan object being estimated with an electrode on the scalp. The brainwaverecording sensor also outputs the result of measurement, brainwave data.Brainwave data can be augmented as needed.

Other Embodiments

The differentiation apparatus with a process of contracting featuresaccording to one or more embodiments of the present disclosure has beendescribed based on the embodiments, but the present disclosure is notlimited to such embodiments. Various modifications applied to thepresent embodiments and embodiments constructed by combining constituentelements in different embodiments that are conceivable to those skilledin the art are also encompassed within the scope of one or moreembodiments of the present disclosure as long as such embodiments do notdeviate from the intent of the present disclosure.

For example, a peak to peak value can be used as the amplitude value ofbrainwave data in each of the embodiments described above, but theamplitude value is not limited thereto. For example, a simple peak valuecan be used as the amplitude value.

In the embodiment described above, the range of the value of magnitudeof the degree of unpleasantness is envisioned to be set so that thevalue of Pmax, which is the magnitude of the degree of unpleasantnesscorresponding to the upper limit value Amax of a feature such asbrainwave amplitude or a combination thereof, would be 1, or the valueof Pmin, which is the magnitude of pain corresponding to the lower limitvalue Amin of the feature or combination thereof, would be 0, but therange of values is not limited thereto. For example, the magnitude ofpain can be represented by 0 to 100. In such a case, the paindifferentiation/estimation unit 104000 can estimate the value Px ofmagnitude of pain, when shown by the pain level visualization unit107000, by the following equation.

Px=Pmax×(Ax−Amin)/(Amax−Amin)

Curve fitting including sigmoid fitting was described above as anexample of generating a pleasant/unpleasant determination value byanalyzing a plurality of brainwave data, but this is not a limitingexample. A predetermined value can also be used as the upper limit valueof a brainwave amplitude. The predetermined value (absolute value) isfor example 50 μV to 100 μV, which can be experimentally or empiricallydetermined. In such normal analysis, data from about plus or minus 50 μVto 100 μV is eliminated as an artifact removal method. Such artifactremoval can also be performed in the present disclosure as needed.

Any type of stimulation can be applied as stimulation applied to theobject 106000 by the reference stimulation application unit 105000 (seeFIG. 15) as long as the magnitude of the degree of unpleasantness sensedby the object 106000 changes in accordance with the type of stimulationor application environment.

Some or all of the constituent elements of the apparatus of the presentdisclosure in each of the embodiments described above can be comprisedof a single system LSI (Large Scale Integration). For example, as shownin FIG. 15, the apparatus 108000 can be comprised of the featurecontracting unit 101000, pain differentiation/estimation modelgeneration unit 103000, pain differentiation/estimation unit 104000, andpain level visualization unit 107000, as well as a system LSI having thefeature extraction unit 102000 and the reference stimulation applicationunit 105000. In this system LSI or the like, the sample augmentation ofthe present disclosure may be carried out, wherein the function ofsample augmentation may be installed in each unit such as a featurecontracting unit and a different unit that is not separately shown(sample augmentation unit) may be set to carry out sample augmentationin said unit.

System LSI is ultra-multifunctional LSI manufactured by integrating aplurality of constituents on a single chip, or specifically a computersystem comprised of a microprocessor, ROM (Read Only Memory), RAM(Random Access Memory) and the like.

A computer program is stored in a ROM. The system LSI accomplishes itsfunction by the microprocessor operating in accordance with the computerprogram.

The term system LSI is used herein, but the term IC, LSI, super LSI, andultra LSI can also be used depending on the difference in the degree ofintegration. The methodology for forming an integrated circuit is notlimited to LSI. An integrated circuit can be materialized with adedicated circuit or universal processor. After the manufacture of LSI,a programmable FPGA (Field Programmable Gate Array) or reconfigurableprocessor which allows reconfiguration of the connection or setting ofcircuit cells inside the LSI can be utilized.

If a technology of integrated circuits that replaces LSI by advances insemiconductor technologies or other derivative technologies becomesavailable, functional blocks can obviously be integrated using suchtechnologies. Application of biotechnology or the like is also apossibility.

One embodiment of the present disclosure can be not only such a paindifferentiation/estimation model generation, sustained paindifferentiation/estimation unit, but also a pain classifier generation,pain differentiation/classification method using characteristicconstituent units contained in a pain estimation apparatus as steps.Further, one embodiment of the present disclosure can be a computerprogram for implementing each characteristic step in featurecontracting, feature extraction, pain differentiation/estimation modelgeneration, and pain differentiation/estimation on a computer. Oneembodiment of the present disclosure can also be a computer readablenon-transient recording medium on which such a computer program isrecorded.

In each of the embodiments described above, each constituent element canbe materialized by being configured with a dedicated hardware or byimplementing software program that is suited to each constituentelement. Each constituent element can be materialized by a programimplementation unit such as a CPU or a processor reading out andimplementing a software program recorded on a recording medium such as ahard disk or semiconductor memory. In this regard, softwarematerializing the pain estimation apparatus of each of the embodimentsdescribed above or the like can be a program such as those describedbelow.

(Embodiment Using Cloud, IoT, and AI)

The pain determination technology of the present disclosure can beprovided in a form comprising all constituents as a single system orapparatus (see FIG. 16). Alternatively, a pain differentiation apparatuscan also be envisioned in a form of mainly measuring brainwaves anddisplaying results while calculation or differentiation modelcalculation are performed on a server or cloud (see FIGS. 17, 18, and19). Some or all of them can be performed using IoT (Internet of Things)and/or artificial intelligence (AI).

Alternatively, a pain differentiation apparatus can also be envisionedin a semi-standalone form where a differentiation model is stored andperforms differentiation therein, but main calculation such ascalculation of a differentiation model is performed on a server or cloud(FIG. 19). Since transmitting/receiving is not necessarily alwayspossible at some locations where the apparatus is implemented such ashospitals, this is a model envisioned for use when communication isblocked.

Therefore, in one aspect, the present disclosure provides a program forimplementing a method of differentiating pain of an object on acomputer, the method comprising: a) obtaining brainwave data or analysisdata thereof from the object; b) generating a differentiation modelbased on the brainwave data or analysis data thereof; and c)differentiating pain by fitting the brainwave data or analysis datathereof from the object to the differentiation model, and a recordingmedium, system, and apparatus storing the same. The sample augmentationof the present disclosure may be provided to be performed in thisprogram.

A system that materializes such a program is materialized in anembodiment that deems the entirety as a system. In this aspect, thepresent disclosure is a system for differentiating pain of an object,the system comprising: X) a brainwave data obtaining unit for obtainingbrainwave data or analysis data thereof from an object; Y) a paindifferentiation/estimation model generation unit for generating adifferentiation model based on the brainwave data or analysis datathereof; and Z) a pain differentiation/estimation unit fordifferentiating pain by fitting the brainwave data or analysis datathereof from the object to the model. In such a case, the brainwave dataobtaining unit is illustrated as a brainwave data measurement unit110000 and brainwave feature extraction unit 140000, as schematicallyexemplified in FIG. 16. The pain differentiation/estimation modelgeneration unit is depicted as a pain differentiation model generationunit 160000. Furthermore, the pain differentiation/estimation unit isdepicted as a pain level differentiation estimation unit 150000. In FIG.16, a pain level visualization unit 130000 and data storage unit 170000are also depicted. Such a visualization unit and storage unit are notnecessarily essential, but it is preferable that they are comprised whenprovided as a medical equipment. The sample augmentation of the presentdisclosure may also be carried out in a system, wherein the function ofsample augmentation may be installed in each unit such as the brainwavedata obtaining unit and a different unit that is not separately shown(sample augmentation unit) may be set to carry out sample augmentationin said unit.

A visualization unit can be any unit, as long as a user can recognizethe result of differentiating pain. An input/output apparatus, display,television, monitor, or the like can be used. Instead of a visualizationunit, another recognition means can be used such as audio. A soundgeneration apparatus (e.g., speaker), vibration apparatus, electrode, orother apparatuses that can present a challenge to a subject can becomprised.

A storage unit can be a recording medium such as a CD-R, DVD, Blueray,USB, SSD, or hard disk. A storage unit can be stored in a server or anappropriate recording form on the cloud.

As schematically exemplified in FIG. 16, the present apparatus can beused when creating or determining a differentiation model (white arrow)and when monitoring actual pain. When creating/determining a model, thebrainwave data measurement unit 110000 obtains brainwaves when aplurality of pain stimulations is applied to an object. Recorded data istransmitted to the brainwave feature extraction unit 140000 to create aplurality of features. The features are transmitted to the paindifferentiation model generation unit 160000 to generate adifferentiation algorithm. Upon doing so, a plurality of features may beaugmented by the above-mentioned system 100 to transmit the augmentedfeatures to the pain differentiation model generation unit 160000 togenerate a differentiation algorithm. The differentiation algorithm istransmitted to the pain level differentiation estimation unit 150000,and differentiation results from the created model are transmitted tothe pain level visualization unit 130000 and displayed for reviewing theappropriateness of pain level differentiation results. After thedifferentiation model is determined, real-time monitoring of actual painlevels occurs through the flow of black arrows. Specifically, once painmonitoring starts, brainwave data is obtained by the brainwave datameasurement unit 110000 from an object, and the brainwave data istransmitted to the brainwave feature extraction unit 140000 to extract aplurality of brainwave features. The created features are transmitted tothe pain level differentiation estimation unit 150000, and thedifferentiation results are displayed on the pain level visualizationunit 130000. The processes can be combined. If differentiation resultsare not appropriate upon real-time monitoring, results of the pain leveldifferentiation estimation unit 150000 are fed back to the paindifferentiation model generation unit 160000. After the model iscorrected, the corrected model is retransmitted to the pain leveldifferentiation estimation unit 150000. The sample augmentation of thepresent disclosure may also be carried out in this apparatus, whereinthe function of sample augmentation may be installed in each unit suchas a brainwave data measurement unit and a different unit that is notseparately shown (sample augmentation unit) may be set to carry outsample augmentation in said unit.

In one aspect, based on FIG. 17, a terminal only obtains brainwaves, andthe present disclosure is a system for differentiating pain of anobject, the system comprising X) a pain differentiation terminal and Y)a pain differentiation/estimation server, wherein the paindifferentiation terminal comprises: X-1) a brainwave data obtainingterminal for obtaining brainwave data or analysis data thereof from anobject; and X-2) a module for transmitting and receiving the brainwavedata or analysis data thereof and a differentiation result to a paindifferentiation/estimation server, wherein the paindifferentiation/estimation server comprises: Y-1) a paindifferentiation/estimation model generation module for generating adifferentiation model based on the brainwave data or analysis datathereof; Y-2) a pain differentiation/estimation module for generating adifferentiation result from differentiating pain by fitting thebrainwave data or analysis data thereof from the object to the model;and Y-3) a differentiation result transceiver module for transmittingand receiving the brainwave data or analysis data thereof and thedifferentiation result. The sample augmentation may also be carried outin this system, wherein the function of sample augmentation may beinstalled in an existing module and a different module that is notseparately shown (sample augmentation module) may be set to carry outsample augmentation in said module.

As schematically exemplified in FIG. 19, the present apparatus can beused when creating or determining a differentiation model (white arrow)and when monitoring actual pain. When creating/determining a model, thebrainwave data measurement unit 110000 obtains brainwaves when aplurality of pain stimulations is applied to an object. Recorded data isdisplayed online at the pain level visualization unit 130000 andtransmitted to the brainwave feature extraction unit 140000 to create aplurality of features. The created features are transmitted, through adata transceiver unit 120000, to a data transceiver unit 125000 of acloud/server and to the pain differentiation model generation unit160000. Upon doing so, a plurality of features may be augmented by theabove-mentioned system 100 to transmit the augmented features to thedata transceiver unit 125000 of a cloud/server through the datatransceiver unit 120000 for transmission to the pain differentiationmodel generation unit 160000. The created model is ultimately stored inthe pain level differentiation estimation unit. At this time, theoutputted differentiation/estimation results are transmitted to theexternal data transceiver unit 120000 through the data transceiver unit125000 and displayed at the pain level visualization unit 130000 forreviewing. After the differentiation model is determined, real-timemonitoring of actual pain levels occurs through the flow of blackarrows. Specifically, once pain monitoring starts, brainwave data isobtained by the brainwave data measurement unit 110000 from an object,and the brainwave data is displayed at the pain level visualization unit130000 and simultaneously transmitted to the brainwave featureextraction unit 140000 to extract a plurality of brainwave features. Thecreated features are augmented by the above-mentioned system 100 andtransmitted to the pain level differentiation estimation unit 150000through the data transceiver units 120000 and 125000 for differentiationand estimation of pain levels. The differentiation results are displayedon the pain level visualization unit 130000 through the data transceiverunits 120000 and 125000. The processes can be combined. Ifdifferentiation results are not appropriate upon real-time monitoring,results of the pain level differentiation estimation unit 150000 are fedback to the pain differentiation model generation unit 160000. After themodel is corrected, the corrected model is retransmitted to the painlevel differentiation estimation unit 150000. The recorded data orcreated feature or differentiation model is stored in the data storageunit 170000 when appropriate.

In another aspect, “brain feature”=analysis data extracting module is ina separated form based on FIG. 17. A feature is presumed to becalculated on the terminal side. In this regard, the present disclosureprovides a system for differentiating pain of an object, the systemcomprising X) a pain differentiation terminal and Y) a paindifferentiation/estimation server, wherein the pain differentiationterminal comprises: X-1) a brainwave data obtaining terminal forobtaining brainwave data from an object; and X-2) a module fortransmitting and receiving the brainwave data and a differentiationresult to the pain differentiation/estimation server, wherein the paindifferentiation/estimation server comprises: Y-1) a paindifferentiation/estimation model generation module for generating adifferentiation model based on the brainwave data or analysis datathereof; Y-1′) a brainwave feature extraction module for extractinganalysis data from the brainwave data; Y-2) a paindifferentiation/estimation module for generating a differentiationresult from differentiating pain by fitting the brainwave data oranalysis data thereof from the object to the model; and Y-3) adifferentiation result transceiver module for transmitting and receivingthe brainwave data or analysis data thereof and the differentiationresult. The sample augmentation of the present disclosure may also becarried out in this case, wherein the function of sample augmentationmay be installed in an existing module and a different module that isnot separately shown (sample augmentation module) may be set to carryout sample augmentation in said module.

As schematically exemplified in FIG. 18, the present apparatus can beused when creating or determining a differentiation model (white arrow)and when monitoring actual pain. When creating/determining a model, thebrainwave data measurement unit 110000 obtains brainwaves when aplurality of pain stimulations is applied to an object. Recorded data isdisplayed online at the pain level visualization unit 130000 andtransmitted to the data transceiver unit 125000 of a cloud serverthrough the data transceiver unit 120000 and to the brainwave featureextraction unit 140000 to create a plurality of features. The createdfeatures are transmitted to the pain differentiation model generationunit 160000. Upon doing so, a plurality of features may be augmented bythe above-mentioned system 100 to transmit the augmented features to thepain differentiation model generation unit 160000. The created model isultimately stored in the pain level differentiation estimation unit150000. At this time, the outputted differentiation/estimation resultsare transmitted to the external data transceiver unit 120000 through thedata transceiver unit 125000 and displayed at the pain levelvisualization unit 130000 for reviewing. After the differentiation modelis determined, real-time monitoring of actual pain levels occurs throughthe flow of black arrows. Specifically, once pain monitoring starts,brainwave data is obtained by the brainwave data measurement unit 110000from an object, and the brainwave data is displayed at the pain levelvisualization unit 130000 and simultaneously transmitted to thebrainwave feature extraction unit 140000 through the data transceiverunits 120000 and 125000 to extract a plurality of brainwave features.The created features are augmented by the above-mentioned system 100 andtransmitted to the pain level differentiation estimation unit 150000 fordifferentiation or estimation of pain levels. The differentiationresults are displayed on the pain level visualization unit 130000through the data transceiver units 120000 and 125000. The processes canbe combined. If differentiation results are not appropriate uponreal-time monitoring, results of the pain level differentiationestimation unit 150000 are fed back to the pain differentiation modelgeneration unit 160000. After the model is corrected, the correctedmodel is retransmitted to the pain level differentiation estimation unit150000. The recorded data or created feature or differentiation model isstored in the data storage unit 170000 when appropriate.

In still another aspect, an embodiment of a terminal obtainingbrainwaves and fitting the brainwaves to a stored model is provided. Inthis aspect, the present disclosure provide a system for differentiatingpain of an object, the system comprising X) a pain differentiationterminal and Y) a pain differentiation/estimation server, wherein thepain differentiation terminal comprises: X-1) a brainwave data obtainingterminal for obtaining brainwave data or analysis data thereof from anobject; X-2) a module for transmitting and receiving the brainwave dataor analysis data thereof and a differentiation model to the paindifferentiation/estimation server; and X-3) a differentiation modelmodule for storing a differentiation model, wherein pain isdifferentiated by fitting the brainwave data or analysis data thereoffrom the object to the differentiation model, wherein the paindifferentiation/estimation server comprises: Y-1) a paindifferentiation/estimation model generation module for generating adifferentiation model based on the brainwave data or analysis datathereof; Y-2) a model transmission module for transmitting thedifferentiation model to the pain differentiation terminal; andoptionally a brainwave feature extraction module for extracting analysisdata from the brainwave data.

As schematically exemplified in FIG. 19, the present apparatus can beused when creating or determining a differentiation model (white arrow)and when monitoring actual pain. When creating/determining a model, thebrainwave data measurement unit 110000 obtains brainwaves when aplurality of pain stimulations is applied to an object. Recorded data isdisplayed online at the pain level visualization unit 130000 andtransmitted, through the data transceiver unit 120000, to the datatransceiver unit 125000 of a cloud server and to a brainwave featureextraction unit 145000 in a cloud server to create a plurality offeatures. Upon doing so, a plurality of features may be augmented by theabove-mentioned system 100 to transmit the augmented features to thepain differentiation model generation unit 160000. The created featuresare transmitted to the pain differentiation model generation unit160000. The created model is ultimately stored in the pain leveldifferentiation estimation unit 150000. At this time, the outputteddifferentiation/estimation results are transmitted to the external datatransceiver unit 120000 through the data transceiver unit 125000 anddisplayed at the pain level visualization unit 130000 for reviewing.After the differentiation model is determined, real-time monitoring ofactual pain levels occurs through the flow of black arrows.Specifically, once pain monitoring starts, brainwave data is obtained bythe brainwave data measurement unit 110000 from an object transmitted tothe brainwave feature extraction unit 140000 to extract a plurality ofbrainwave features. The created features are transmitted to the painlevel differentiation estimation unit 150000 for differentiation orestimation of pain levels and displayed at the pain level visualizationunit 130000. The processes can be combined. If differentiation resultsare not appropriate upon real-time monitoring, results of the pain leveldifferentiation/estimation unit 150000 are fed back to the paindifferentiation model generation unit 160000. After the model iscorrected, the corrected model is retransmitted to the pain leveldifferentiation estimation unit 150000. The recorded data or createdfeature or differentiation model is stored in the data storage unit170000 when appropriate. The sample augmentation of the presentdisclosure may also be carried out in this system, wherein the functionof sample augmentation may be installed in an existing brainwave datameasurement unit and a different module that is not separately shown(sample augmentation unit) may be set to carry out sample augmentationin said module.

“Software as service (SaaS)” mostly falls under such a cloud service.Since a pain differentiation apparatus at the early stages is understoodto be installed with a differentiation algorithm made from data in alaboratory environment, the apparatus can be provided as a systemcomprising two or three features of these embodiments.

For example, the following is contemplated.

1. A function for incorporating brainwave data is included on theterminal side (brainwave data measurement unit 110000 in FIGS. 16 to19).2. An apparatus can have a function for extracting a feature, or thefeature can be included on the terminal side (e.g., brainwave dataitself is the responsibility of another equipment, which provides afeature to a terminal by transmission/receipt.) In this regard, thebrainwave feature extraction unit 140000 or 145000 (FIGS. 16 to 19) canbe on the terminal side or the server side, or integrated with thebrainwave data measurement unit or provided separately. FIGS. 16 to 19show embodiments comprising them separately. The sample augmentation ofthe present disclosure may be carried out at a stage somewhere in theseseries of processes, wherein the function of sample augmentation may beinstalled in an existing brainwave data measurement unit and a differentmodule that is not separately shown (sample augmentation unit) may beset to carry out sample augmentation in said module.

For example, basic features such as potential, frequency, potentialcorrelation, and entropy can be stored as standard specification, andother features for increasing differentiation accuracy or the like canbe stored as options.

A pain differentiation model is generated in the pain differentiationmodel generation unit 160000. At the pain differentiation modelgeneration unit, a pain differentiation model is generated based onbrainwave data or analysis data thereof, and the pain differentiationmodel is transmitted to a pain level differentiation estimation unit.

For example, a standard (general differentiation model with standinstallation) as well as options can be included in a paindifferentiation model generation unit. For example, the paindifferentiation model generation unit can comprise option 1 (tailor-madedifferentiation model, where price changes depending on the extent ofmodel creation), option 2 (creation of a facility dedicateddifferentiation model), option 3 (client requested setting), option 4(model creatable by the clients themselves), option 5 (increase in thenumber of created differentiation models), or the like.

A preferred embodiment may comprise a function for improving adifferentiation model. This function can be in a pain differentiationmodel generation unit, or comprised as a separate module. Such adifferentiation model improving function can comprise options such asoption 1 (period of 1 year, 1 to 2 times a year), option 2 (period of 1year, once every 1 or 2 months), option 3 (extended period, once ortwice a year), or option 4 (extended period+1, once every 1 or 2months).

Data can be stored as needed. Data storage is generally equipped on thesever side (data storage unit 170000 in FIGS. 17 to 19), but datastorage can be at the terminal side for not only fully equipped modelsbut also for cloud models (not shown due to being optional). When aservice is provided on the cloud, options such as standard (e.g., up to10 Gb on the cloud), option 1 (e.g., additional 1 Tb on the cloud),option 2 (parameter is set for divided storage on the cloud), and option3 (stored by differentiation model on the cloud) can be provided fordata storage. Data is stored, and data is imported from all soldapparatuses to create big data (e.g., brainwave database 180000), and adifferentiation model is continuously updated or a new model isconstructed so that new differentiation model software such as “burnpain differentiation model” can be provided.

There can also be data analysis options. In this regard, a patternclassification of patients (search for a patient cluster based on achange in patters of feature or differentiation accuracy) or the likecan be provided. In other words, this can be envisioned as an option fora calculation method of the pain differentiation model generation unit160000.

(Brainwave Feature Correlation)

In another aspect, the present disclosure provides a method ofdifferentiating or determining pain using brainwave feature correlation.Examples of brainwave features targeted for correlation include, but arenot limited to, raw brainwave data and analysis data thereof (alsoreferred to as processed feature) such as potential, frequency power,amplitude (e.g., mean amplitude, complexity index (entropy or the like))and the like. It was found that any of them can be used as an indicatorof functional connectivity by studying the correlation betweenelectrodes. A more desirable method estimates the intracranial sourcefrom scalp data and studying the correlation between sites. If recordingelectrodes are limited, it is inferred from the correlation betweenelectrodes described above. While “brainwave feature correlation” isalso referred to as “brainwave relation feature”, they have the samemeaning. Examples of features that can be used for brainwave featurecorrelation or the like include, but are not limited to, “potentialcorrelation”, “phase synchronization (phase locking value)” and thelike. Coherence and the like can also be used.

It was found in the present disclosure that correlation of processedfeatures such as frequencies and entropy can also be used, besidesbrainwave potential correlation, in differentiating pain. Since they canbe used as an indicator of function connectivity by observingcorrelation between electrodes, “brainwave feature correlation” itselfcan be used more broadly in differentiating pain.

As correlation, “potential correlation”, “phase synchronization (phaselocking value)” or the like can be preferably used in the Examples, butcoherence that has been used conventionally or the like can also beused.

In one embodiment, the brainwave feature used in the present disclosurecan be a plurality of brainwave features from the same electrode orbrainwave features from different electrodes when calculatingcorrelation. Thus, the brainwave feature correlation used in the presentdisclosure is correlation between brainwave features at the same ordifferent electrodes. When brainwave features at the same electrode areused, features can be from the same category (e.g., frequency power,mean amplitude, complexity index, or the like) of features fromdifferent categories. When using brainwave features at differentelectrodes, features can be from the same category (e.g., frequencypower, mean amplitude, complexity index, or the like) of features fromdifferent categories. Brainwave features that can be used may be simplythe extracted raw brainwave data itself or processed data thereof.Examples of brainwave features that can be used include, but are notlimited to, potential, frequency power, mean amplitude, complexity index(e.g., MSE), and the like. Examples of correlation include, but are notlimited to, potential frequency and phase synchronization (phase lockingvalue). Coherence can also be used. Coherence and phase synchronization(phase locking value) are phase synchronicity of the same frequency ordifferent frequencies between or in electrodes, and they can be used inthe same manner. Coherence is a phase synchronization indicator that iscalculated based on the premise of signals being stable (overallactivity property is also consistently materialized locally) and linear.However, prior art is not used after verifying the premises in manycases. Meanwhile, phase synchronization (phase locking value (PLV)) candetect phase synchronization properties more strongly, with no premisepresumed by coherence. Thus, in one embodiment herein, phasesynchronization (PLV) can be more preferable than coherence.Alternatively, coherence and PLV can both be used, where they can beused as a multiple choice system that uses one that better approximatesa binomial classification pattern of sigmoid or step function or usesboth alternatingly (Coh×PLV).

A feature exhibiting a binomial classification pattern (e.g., sigmoidalfeature) is a form of “discrete feature”, i.e., feature exhibiting abinomial distribution property. Such a double-peak distribution is alsoobserved in pain estimation values (see FIG. 12), so that this isinvolved in the principal mechanism of pain differentiation. In actualpain monitoring, a distribution property of features for no pain andhaving pain can be studied by applying a plurality of pain teststimulations to an object. FIG. 23 shows a schematic diagram fromfunction or curve fitting to plotting of a histogram of a feature. Thenumerical value where distributions of samples for no pain and havingpain intersect is used as a “differentiation threshold value”. Samplesless than the threshold value are converted to a category scale of “−1”and samples that are greater than the threshold value are converted to acategory scale of “1” using this threshold value for each feature. Theconverted samples can be inputted into a differentiation model. Poincaredistribution or the like can also be used as the distribution fordetermining such a threshold value.

In various embodiments, the present disclosure is intended to bedirected to broadly defined “interrelation” including synchronicity andnarrowly defined relation expressed by a correlation coefficient (r orp). In this regard, correlation can include various embodiments such assynchronicity, connectivity, unrelatedness, delay, positive/negative,similarity, and match. Examples thereof include temporal correlation,spatial correlation, spatiotemporal synchronicity, special relationshipor connectivity, unrelatedness or uncorrelatedness, delay or breakdownin temporal correlation, positive/negative or correlated property,similarity or level of correlation coefficient, and match or completecorrelation. It can be understood that synchronicity is temporalcorrelation, connectivity is spatial (e.g., parts of brain)relationship, unrelatedness is uncorrelated, delay is breakdown intemporal correlation, positive/negative is correlated property,similarity is high correlation coefficient, and match is completecorrelation.

In one embodiment, the brainwave feature correlation used in the presentdisclosure is correlation of brainwave features between differentelectrodes. It was not conventionally known that correlation ofbrainwave features between different electrodes is useful fordifferentiation or determination of pain. The frontal-occipitalpotential correlation is between electrodes. In addition for correlationof frequency power, correlation between different frequencies in thesame electrodes can be the feature correlation.

It has been elucidated that anesthesia and pain can be distinguished byobserving the correlation between different electrodes in the presentdisclosure. In this regard, it is generally understood that functionalconnectivity decreases when consciousness decreases due to the effect ofanesthesia. Meanwhile, as demonstrated in the present disclosure, it wasfound that the frontal-parietal potential correlation decreases whenpain at the highest level of Level 6 (50° C.) exemplified in theExamples becomes unbearable. This is understood to explain functionalconnectivity. It was found that activity correlation breaks down with adecrease in consciousness while activity correlation breaks down whenpain is strong, which was unexpected. It can be understood as a resultof further study thereon that activity is fragmented from a decrease inconsciousness due to anesthesia: decrease in overall cortex activity,and consciousness is extremely focused on pain if pain is excessive,resulting in fragmentation due to excessive local increase in theintensity of activity of the front portion of the prefrontal portion. Inthis manner, this can be interpreted as break down in correlation due to“Hypo-activity” for anesthesia and “Hyper-activity” for excessive pain.The differentiation of the present disclosure can distinguish such adifference between hypo and hyper. For example, functional connectivitybreaks down and correlation decreases due to anesthesia, so that it isinconsistent to predict that a strong pain level similarly results indecreased correlation. Thus, the findings in the present disclosurewould not have been readily expected. In other words, increased paingenerally elicits attention so that functional connectivity betweensites should rather be understood to increase. Despite of the above, adecrease in frontal-parietal potential correlation when pain isexcessive in the present disclosure is different from intuition. In thisregard, the present disclosure is interpreted so that a “similar”correlation decrease can be understood with vectors in differentdirections based on the concept of hyper and hypo.

In one embodiment, if correlation of brainwave features at two or moredifferent electrodes is used, the different electrodes preferably have arelationship of being positioned relatively in front and back of a head.Pain can be more effectively differentiated by observing theinterrelation of brainwave features with such a positional relationship.The relative back and front relationship can be determined by therelationship that is generally understood in the art. When an absolutepositional relationship is to be identified, a line connecting ear lobescan be used for the determination.

In one embodiment, at least one of the different electrodes is at afront portion of a head. In another embodiment, at least one of thedifferent electrodes is at a back portion of a head. In still anotherembodiment, at least one of the different electrodes is at a frontportion of ahead, and another electrode is at a back portion of thehead. The “front portion” of the head refers to an area in front of aline connecting the left and right ear lobes, and “back portion” of thehead refers to an area behind a line connecting the left and right earlobes. Electrodes at the center portion on the scalp with a name of C orT alone can be included in either or both the front and back portions.In a preferred embodiment, electrodes at the center portion on the scalpon a line connecting earlobes with a name of C or T alone are excluded,but if the electrode in the center portion is relatively behind theelectrode in the front portion, or relatively in front of the electrodein the back portion, the electrodes can be used for extraction of acorrelation feature. This is not a limiting example.

In one preferred embodiment, the correlation used in the presentdisclosure comprises correlation between an electrode in a front portionof a head and an electrode in a back portion of the head. In anotherpreferred embodiment, the correlation used in the present disclosurecomprises correlation between an electrode in a frontal portion and anelectrode in a parietal portion.

In one specific embodiment, the brainwave features used in correlationused in the present disclosure comprise at least one feature at thefrontal portion such as the frontal pole Fp1, frontal pole Fp2, frontalportion F3, frontal portion F4, anterior-temporal portion F7,anterior-temporal portion F8, and midline frontal portion Fz andadjacent sites, and at least one feature at the parietal portion such asmidline parietal portion Pz, parietal portion P3, and parietal portionP4. In one specific embodiment, the electrodes used can comprise, butnot limited to, electrodes at at least one of the frontal pole Fp1,frontal pole Fp2, frontal portion F3, and frontal portion F4 andparietal electrode Pz.

In one embodiment, “front-back signal correlation” can be used, whichuses a brainwave recording electrode worn at the front portion from aline connecting the left and right ear lobes as an electrode at thefront portion, and uses a brainwave recording electrode warn at the backportion from the line connecting the left and right ear lobes as anelectrode at the back portion. As an embodiment of such a “front-backsignal correlation”, electrodes comprising an “electrode at the frontalportion” and an “electrode at the parietal portion” are used. The“parietal portion” is encompassed by “back portion”, and “back” caninclude the parietal portion as well as the central portion andoccipital portion, but this can be only the parietal portion and theoccipital portion. Alternatively, mid-temporal and posterior temporalportions can also be encompassed by back portion.

In one embodiment, the brainwave feature comprises at least one selectedfrom the group consisting of a potential (mean amplitude or the like),frequency power, and a complexity index. In a preferred embodiment,potential is included as a brainwave feature. In such a case, thebrainwave feature correlation is also referred to as potentialcorrelation.

In one embodiment, the mean amplitude that can be used in the presentdisclosure is dependent on a stimulation application time or painduration, but can be a mean value of an absolute value of amplitudeduring 15 seconds after application of stimulation as shown in theExamples. When seven electrodes are used, there would be a mean of sevenamplitude absolute values for each level of stimulation.

In one embodiment, the frequency power comprises at least one of 5bandwidths δ, θ, α, β, and γ in Fp1, Fp2, F3, F4, C3, C4, and Pz.

In one embodiment, the method of the present disclosure comprisesgenerating a differentiation model by machine learning using thebrainwave feature. The machine learning used can use any algorithm usedin the art. Upon doing so, the brainwave feature may be augmented by theabove-mentioned system 100 to generate a differentiation model bymachine learning using the augmented brainwave feature.

For example, differentiation model creation using machine learning canbe provided by a method comprising: a) obtaining brainwave data oranalysis data thereof from the object; b) contracting features in thebrainwave data or analysis data thereof with respect to the pain; c)differentiating and analyzing by machine learning and cross validationfrom top of ranking of weighting coefficient (including approximationcoefficient; e.g., regression coefficient) of each feature after thecontracting or combination thereof; and d) determining a model thatattains a given accuracy.

(Medical Apparatus/System/Program)

In one aspect, the present disclosure provides an apparatus forevaluating or determining pain experienced by an object, the apparatuscomprising: A) a headset comprising at least one electrode for obtaininga brainwave signal; and B) a base unit, wherein the base unit calculatesa parameter comprising at least one selected from the group consistingof brainwave feature correlation and/or a complexity index of abrainwave, wherein a differentiation model correlating the parameterwith a pain level of the object is generated, and wherein the pain levelof the object is calculated and displayed by applying the parameter ofthe object to the differentiation model.

Any headset can be used herein, as long as the headset can be worn onthe head and measure brainwaves. Examples thereof include, but notlimited to, wireless head gear shaped headsets with electrodes attachedthereto. Any base unit can be used, as long as the function describedabove can be materialized. The base unit used can have any shape used innormal medical equipment or device. For example, a base unit cancomprise portions that receive, analyze, differentiate, and displaybrainwave signals from a headset.

In one aspect, the present disclosure provides a computer program formaking an apparatus implement a process for evaluating or determiningpain experienced by an object, the process: calculating a parametercomprising at least one selected from the group consisting ofcorrelation of brainwave features of a brainwave and/or a complexityindex of a brainwave; generating a differentiation model for correlatingthe parameter with a pain level of the object; and calculating anddisplaying the pain level of the object by applying the parameter of theobject to the differentiation model.

In another aspect, the present disclosure provides a recording mediumstoring a computer program for making an apparatus implement a processfor evaluating or determining pain experienced by an object, theprocess: calculating a parameter comprising at least one selected fromthe group consisting of correlation of brainwave features of a brainwaveand/or a complexity index of a brainwave; generating a differentiationmodel for correlating the parameter with a pain level of the object; andcalculating and displaying the pain level of the object by applying theparameter of the object to the differentiation model.

In still another aspect, the present disclosure provides a method ofevaluating or determining pain experienced by an object, the methodcomprising: calculating a parameter comprising at least one selectedfrom the group consisting of correlation of brainwave features and/or acomplexity index of a brainwave; generating a differentiation model forcorrelating the parameter with a pain level of the object; andcalculating and displaying the pain level of the object by applying theparameter of the object to the differentiation model.

It is understood that each of the brainwave feature correlation andcomplexity index of a brainwave used in the apparatus, program,recording medium, and method of the present disclosure can use anyembodiment described in the sections of (Brainwave feature correlation)and (Complexity index).

Each step for differentiation of pain using correlation of brainwavefeatures or a complexity index of brainwave is described hereinafter.

A method of differentiating and analyzing using correlation of brainwavefeatures or a complexity index is described hereinafter using aschematic diagram (FIG. 20).

a) First, the differentiation properties of features of brainwave dataor analysis data thereof or the like or brainwave feature correction orcomplexity index are quantified (S30010). Specifically, this step fits afeature, or brainwave feature correction or complexity index, using asigmoid function of two value change pattern “0, 1” to calculate a modelapproximation index (e.g., R² value) for each individual. This step canbe considered a step for contracting features, or brainwave featurecorrection or complexity index, with respect to pain. This step can beused as a step for determining a threshold value or determination indexin a model curve obtained by fitting when the objective is todifferentiate or estimate individual features, or brainwave featurecorrection or complexity index. In other words, a threshold value can bedetermined by a numerical value such as a threshold potential and usedas a determination index. More specifically, adifferentiation/estimation model is created for 2, 3, or 4classifications or more in accordance with conditional parameters usinga feature, or brainwave feature correction or complexity index. As onemethod, a plot diagram is created and applied (fitted) to an appropriatefitting function such as a sigmoid function pattern or a step function.Any methodology that is known in the art can be used for fitting.Specific examples of such fitting functions include, but are not limitedto, the functions described above, as well as a Boltzmann function,double Boltzmann function, Hill function, logistic dose response,sigmoid Richards function, sigmoid Weibull function, and the like. Astandard logistic function is particularly called a sigmoid function. Astandard function or a modified form thereof is common and preferred.The sample augmentation of the present disclosure may be carried out inany step, wherein the sample augmentation may be carried out afterobtaining brainwave feature, or sample augmentation may be carried outafter carrying out other processing such as contraction.

If a regression coefficient for fitting to an appropriate functionpattern such as the sigmoid function pattern is at or greater than agiven value, a threshold value for determining pain can optionally bedetermined based on the sigmoid curve or the like. In this regard, thiscan be generated based on an inflection point for sigmoid curves, butthis is not limited thereto. As needed, pain classifiers can becalibrated to maximize the pain level classification.

b) Next, features such as brainwave feature correction or complexityindex are ranked (S30020). In this regard, a weighting coefficient canbe used. The mean value of R² values of each parameter or the like canbe used as the weighting coefficient. Once calculation is completed,features such as brainwave feature correction or complexity index areranked.

Next, c) hierarchical differentiation analysis that includes featuressuch as brainwave feature correction or complexity index in order fromtop ranking features is performed (S30030). Examples thereof includeinputting features in order from top ranking features into a machinelearning model such as support vector machine and studying thedifferentiation accuracy of all samples by leave-one-out or 10-foldcross validation and the like. b) and c) correspond to steps fordifferentiating and analyzing by machine learning and cross validationafter inputting the weighting coefficients of each feature aftercontracting or a combination thereof from top of the ranking.

d) Next, a differentiation model is determined (S30040). Thiscorresponds to a step of determining a model that attains a givenaccuracy. For example, a model with the highest accuracy or an“economical differentiation model” with the fewest feature for a modelwith the same accuracy can be determined. However, a setting such asselect any model that attains a give accuracy (e.g., 70% differentiationaccuracy) or the like can be provided. In the present disclosure, stepsc) and d) can be performed in a model generation unit. If it is expectedthat a model is predetermined using a known database, pain data may beinputted during actual monitoring to perform differentiation andestimation. Black arrows are the envisioned flow of actual monitoring.

A method of re-ranking features such as correlation of brainwavefeatures and a complexity index can also be used in a different optimalmodel selection process (see FIG. 21). In such a case, a differentiationmodel is determined in S30040 and then regressed to the feature rankingin S30020, features are re-ranked by a value of difference indifferentiation accuracy of n−1 feature model (n≥2) and n feature model,and features are inputted into the model one at a time from the topfeatures in S30030 to recalculate the differentiation accuracy. Adifferentiation model is then determined through the 2nd modeldetermination process in S30040. These steps are as follows when usingthe symbols in FIG. 21. Specifically, a differentiation model isdetermined then regressed to the feature ranking in S30060, and featuresare re-ranked by a value of difference in differentiation accuracy ofn−1 feature model (n≥2) and n feature model (S30070-1), and features areinputted into the model one at a time from top features in S30070-2 torecalculate the differentiation accuracy. A differentiation model isthen determined through the model determination process in S30080.

A feature can be a feature that is obtained in response to some type ofstimulation (e.g., low temperature stimulation, electrical stimulation,or the like) or obtained in a natural environment, or various brainwavedata, brain activity data, amount of brain activity, amplitude data (EEGamplitude), frequency property, or the like can be used. It was found inthe present disclosure that brainwave feature correction, complexityindex, and the like are prioritized. Such brainwave data can be obtainedusing any methodology that is well known in the art. Brainwave data canbe obtained by measuring electrical signals of a brainwave and isdisplayed by potential (can be displayed by μV or the like) as amplitudedata or the like. Frequency properties are displayed as power spectrumdensity or the like (also referred to as frequency power or the like). Acomplexity index can also be calculated. After basic signal processingof brainwave data such as filtering, eye movement correction, orartifact removal, the data can be associated with a conditionalparameter and a signal of the corresponding portion is extracted tocreate a brainwave feature. This includes mean value (arithmetic mean orgeometric mean), other representative value (median or mode), entropy,frequency power, wavelet, mean and single run event related potentialcomponent, and the like. Further, the correlation of brainwave featurescan be calculated from such brainwave features.

In a preferred embodiment, brainwave data is preferably collected by asimple method, which can 1) use electrodes at a number that is requiredfor analysis, 2) avoid the scalp with hair as much as possible, and 3)record while sleeping, to carry out the present disclosure. Exemplarynumber of electrodes used is, but not limited to, 24, but the number canbe 12 to 24, 24 to 36, 6 to 12, or fewer (e.g., 3, 4, 5, or the like).When brainwave feature correlation is used, brainwave featurecorrelation at any preferred positions described herein can be used.

For contracting, sigmoid fitting, or a step function with stepwiseinflection, a linear function with a continuous stepwise change, or thelike can be used.

As a weighting coefficient, a regression coefficient, or an R² value,correlation coefficient, residual sum of squares (difference betweendifferentiation function and feature), or the like can be used. However,it is important that pain or stress sensed by an individual can bedistinguished with as much accuracy as possible for differentiation ofpain, so that efficacy which is different from detection ofstatistically significant difference can be required or intended.

In one embodiment, brainwave data or analysis data thereof for obtainingthe brainwave feature correction or complexity index used in the presentdisclosure comprises, as data recording positions, frontal-parietalportions such as F3, F4, C3, C4, P3, and P4 in compliance with theinternational 10-20 system or expanded standard thereof, and positionson the scalp over the occipital portion as electrode positions.

Alternatively, a position at a specific uniform distance (e.g., 2.5 cmor the like) can be covered. The duration of recording and analysis canbe, for a short period of event related potential activity, 0 to 100,100 to 200, 200 to 300, 300 to 400, 400 to 500, 500 to 600, 600 to 700,700 to 800 milliseconds (ms), a shorter time segment (10 milliseconds orthe like), or a longer time frame (sometimes spanning several seconds).

In still another embodiment, features such as correlation of brainwavefeatures or a complexity index that can be used comprises a feature inan electrode at at least one selected from the group consisting of Fp1,Fp2, Fpz, F3, F4, Fz, C3, C4, Cz, P3, P4, and Pz, such as mean amplitudeFz, C3, and C4, and frequency Fz (δ), Fz (β), Cz (δ), C3 (θ), and C4(β). A feature can comprise Cz (amplitude), C3 (α), Cz (β), Fz (δ), andCz (γ). In a preferred embodiment, any feature described in the sectionsof (Complexity index) and (Brainwave feature correlation) can be usedherein.

FIG. 24 describes a schematic diagram of the apparatus of the presentdisclosure (differentiation apparatus with a process of contractingfeatures) (301000 to 308000) (it should be noted that some of theconfiguration diagrams are optional constituents that can be omitted).In this schematic diagram, each step is described when appropriate(S30100 to S30600). The sample augmentation of the present disclosuremay be carried out in this apparatus, wherein the function of sampleaugmentation may be installed in an existing feature contracting unitand a different module that is not separately shown (sample augmentationunit) may be set to carry out sample augmentation in said module.

As shown in FIG. 24, this apparatus is comprised of a featurecontracting unit 301000, a feature extraction unit 302000, paindifferentiation/estimation model generation unit 303000, paindifferentiation/estimation unit (can comprise a model correctionprocess) 304000, a reference stimulation application unit 305000, abrainwave data measurement unit 305200, and a pain level visualizationunit 307000. An object is denoted as 306000. A data storage unit 307500can be optionally installed.

In such differentiation with a process of contracting, the number ofpain differentiation/estimation stages (2 stages, 3 stages, or the like)is determined (S30100), and a differentiation function is generated(examples include sigmoid functions in binomial classification and thelike; S30200). A feature is obtained after a reference stimulation(electrical stimulation or the like) is applied to the object 306000from the reference stimulation application unit 305000 in accordancewith the differentiation stage determined at S30100, and a featurerelated to a pain stage is collected (S30300) and contracted (S30400).The collected feature is approximated by a differentiation functiongenerated at S30200 and ranked in accordance with the magnitude of theobtained approximation coefficient (regression coefficient or the like)Features are used in order from the top features. The pain level ofreference stimulation is differentiated and estimated with the paindifferentiation/estimation unit 304000, and a differentiation model withthe number of features with the highest differentiation accuracy is usedfor monitoring pain. This is one embodiment of the process ofcontracting features S30400. A differentiation model (algorithm)installed in a pain differentiation/estimation unit used in the processof contracting (white arrows) and actual pain monitoring process (blackarrows) is created at the pain differentiation/estimation modelgeneration unit 303000, and installed in the paindifferentiation/estimation unit 304000. After completion of thepreprocessing described above at the feature contracting unit 301000,actual pain related brainwave data is collected from the object 306000at the brainwave data measurement unit 305200 comprising anelectroencephalograph or the like (S30500). This is transmitted to thefeature extraction unit 302000 and converted to a feature selected inthe process of contracting amplitudes, frequencies, or the like (e.g.,can be complexity index, brainwave feature correlation, or the like ofspecific electrodes). The extracted parameter is taken into the paindifferentiation/estimation unit 304000 (can comprise a model correctionprocess) from the feature extraction unit 302000, and a pain level isdetermined (S30600). The result of the determination is indicated as atrend of changes or numerical value (e.g., 1 to 100) at the pain levelvisualization unit 307000.

The determination of the pain differentiation/estimation stages atS30100 determines the number of levels to be differentiated or estimated(e.g., 2 stages, 3 stages, or the like).

The generation of a differentiation function at S30200 creates adifferentiation function used in accordance with the number ofdifferentiation levels at S30100 (sigmoid function or step function inbinomial classification or the like).

In the collection of pain stage associated features at S30300, referencestimulation (electrical stimulation or the like) is applied a pluralityof times from the reference stimulation application unit 305000 to theobject 306000 in accordance with the number of levels determined atS30100 to collect related features such as brainwave feature correctionand complexity index.

In contracting of a feature at S30400, a feature obtained at S30300 isapproximated with a differentiation function, features with highapproximation index (e.g., R² value or the like) are ranked, andfeatures are inputted into the pain differentiation/estimation unit304000 in order from top ranking features to differentiate and estimatea level of reference stimulation. A model with a number of features withthe highest differentiation accuracy thereamong is used for actual paindifferentiation/estimation.

For collection of pain related brainwave data at S30500, actual painrelated brainwave data subjected to monitoring of pain is collectedafter completion of the contracting process at the feature contractingunit 301000. This step is data collection in an actual pain monitoringprocess.

For pain level determination at S30600, actual pain related dataobtained at S30500 is processed at the feature extraction unit 302000 toobtain a feature set, which is then differentiated and estimated at thepain differentiation/estimation unit 304000, and a pain level isquantified from an estimated value, and a pain level is determined andmade visible at the pain level visualization unit 307000.

The apparatus 308000 is configured to comprise or to be connected to anelectroencephalograph that is or can be connected to the object(306000), so that brainwave data synchronized with stimulation emittedfrom the reference stimulation application unit 305000 to the object(306000) is obtained at the brainwave data measurement unit 305200. Thisis a summary of the apparatus 308000.

The apparatus 308000 can comprise a brainwave measurement unit, whichinternally comprises or externally connects to a brainwave recordingsensor and optionally a brainwave augmentation unit, and processessignals of a pain related brainwave and differentiates/estimates pain inthe apparatus 308000. The brainwave augmentation unit may be caused toaugment the signal intensity of the brainwave and the processing of theabove-described sample augmentation may be carried out to the brainwave.

In the apparatus 308000, collected brainwave signals are processed toextract a brainwave feature at the feature extraction unit 302000. Uponextraction, a feature contracted in advance at the feature contractingunit 301000 is selected. Further, pain is (optionally) made visible atthe pain level visualization unit 307000. The apparatus internally orexternally comprises the reference stimulation application unit 305000,which applies reference stimulation such as electrical stimulation aplurality of times in accordance with the pain level determined atS30100 in order to contract features that are effective for monitoringpain of the object 306000. Brainwave data related thereto is recorded atthe brainwave data measurement unit 305200, a related brainwave featureis obtained at the feature extraction unit 302000, a pain level ofreference stimulation is differentiated and estimated from the featureat the pain differentiation/estimation unit 304000, and the feature iscontracted S30400 from the result thereof. The reference stimulationapplication unit 305000 also transmits pain stimulation information(stimulation type, environmental information, or the like) fordifferentiating an actual unknown pain level and creating adifferentiator. The reference stimulation application unit 305000optionally comprises a stimulation information visualization unit inaddition to the reference stimulation application unit 305000 and maydisplay information such as an image or number associated with thestimulation or environment. The apparatus 308000 can also internally orexternally comprise the pain differentiation/estimation unit 304000 forgenerating a determination value or differentiator.

In this manner, the apparatus 308000 comprises the brainwave datameasurement unit 305200 and the pain differentiation/estimation unit304000 and optionally the reference stimulation application unit 305000.The apparatus 308000 is materialized, for example, by a computercomprising a processor and a memory. In such a case, the apparatus308000 makes the processor function as the feature contracting unit301000, feature extraction unit 302000, pain differentiation/estimationmodel generation unit 303000, pain differentiation/estimation unit304000, or the like as needed when a program stored in the memory isimplemented by the processor. Stimulation or environmental informationis also made visible as needed. The apparatus 308000 of the presentdisclosure can be materialized, for example, by a dedicated electroniccircuit. A dedicated electronic circuit can be a single integratedcircuit or a plurality of electrical circuits.

The brainwave data obtaining unit and pleasant/unpleasant determinationvalue generation unit can have the same configuration as apleasant/unpleasant determination apparatus.

The feature extraction unit 302000 can also obtain a plurality ofbrainwave data by measuring a brainwave a plurality of times from anobject being estimated via an electroencephalograph (included in thebrainwave data measurement unit 305200). An object is an organism inwhich a change in a brainwave is induced due to stimulation orenvironment, which does not need to be limited to humans.

The pain differentiation/estimation unit 304000 differentiates/estimatesthe degree of unpleasantness using a determination value, and alsogenerates a differentiator of determination value if not generated inadvance externally or internally. The part generating a differentiatoror determination value can be comprised external or internal to theapparatus 308000 as the pain differentiation/estimation unit 304000. Adifferentiation value used for differentiation/estimation of pain is forestimating or classifying the degree of unpleasantness from amplitudesof a plurality of brainwave data. Specifically, the paindifferentiation/estimation unit 304000 or the paindifferentiation/estimation model generation unit 303000 can generate adetermination value for estimating or classifying the degree ofunpleasantness of an object from brainwave data.

A brainwave recording sensor contained in the brainwave data measurementunit 305200 measures electrical activity generated inside the brain ofan object being estimated with an electrode on the scalp. The brainwaverecording sensor also outputs the result of measurement, i.e., brainwavedata.

Brainwave data can be augmented as needed.

Other Embodiments

The differentiation method, program, and apparatus according to one ormore embodiments of the present disclosure has been described based onthe embodiments, but the present disclosure is not limited to suchembodiments. Various modifications applied to the present embodimentsand embodiments constructed by combining constituent elements indifferent embodiments that are conceivable to those skilled in the artare also encompassed within the scope of one or more embodiments of thepresent disclosure as long as such embodiments do not deviate from theintent of the present disclosure.

For example, a peak to peak value can be used as the amplitude value ofbrainwave data in each of the embodiments described above, but theamplitude value is not limited thereto. For example, a simple peak valuecan be used as the amplitude value.

In the embodiment described above, the range of the value of magnitudeof the degree of unpleasantness is envisioned to be set so that thevalue of Pmax, which is the magnitude of the degree of unpleasantnesscorresponding to the upper limit value Amax of a feature such asbrainwave amplitude or a combination thereof, would be 1, or the valueof Pmin, which is the magnitude of pain corresponding to the lower limitvalue Amin of the feature or combination thereof, would be 0, but therange of values is not limited thereto. For example, the magnitude ofpain can be represented by 0 to 100. In such a case, the paindifferentiation/estimation unit 304000 can estimate the value Px ofmagnitude of pain, when shown by the pain level visualization unit307000, by the following equation.

Px=Pmax×(Ax−Amin)/(Amax−Amin)

Curve fitting including sigmoid fitting was described above as anexample of generating a pleasant/unpleasant determination value byanalyzing a plurality of brainwave data, but this is not a limitingexample. A predetermined value can also be used as the upper limit valueof a brainwave amplitude. The predetermined value (absolute value) isfor example 50 μV to 100 μV, which can be experimentally or empiricallydetermined. In such normal analysis, data from about plus or minus 50 μVto 100 μV is eliminated as an artifact removal method. Such artifactremoval can also be performed in the present disclosure as needed.

Any type of stimulation can be applied as stimulation applied to theobject 306000 by the reference stimulation application unit 305000 (seeFIG. 24) as long as the magnitude of the degree of unpleasantness sensedby the object 306000 changes in accordance with the type of stimulationor application environment.

Some or all of the constituent elements of the apparatus of the presentdisclosure in each of the embodiments described above can be comprisedof a single system LSI (Large Scale Integration). For example, as shownin FIG. 24, the apparatus 308000 can be comprised of the featurecontracting unit 301000, pain differentiation/estimation modelgeneration unit 303000, pain differentiation/estimation unit 304000, andpain level visualization unit 307000, as well as a system LSI having thefeature extraction unit 302000 and the reference stimulation applicationunit 305000.

System LSI is ultra-multifunctional LSI manufactured by integrating aplurality of constituents on a single chip, or specifically a computersystem comprised of a microprocessor, ROM (Read Only Memory), RAM(Random Access Memory) and the like.

A computer program is stored in a ROM. The system LSI accomplishes itsfunction by the microprocessor operating in accordance with the computerprogram.

The term system LSI is used herein, but the term IC, LSI, super LSI, andultra LSI can also be used depending on the difference in the degree ofintegration. The methodology for forming an integrated circuit is notlimited to LSI, but can be materialized with a dedicated circuit oruniversal processor.

After the manufacture of LSI, a programmable FPGA (Field ProgrammableGate Array) or reconfigurable processor which allows reconfiguration ofconnection or setting of circuit cells inside the LSI can be utilized.

If a technology of integrated circuits that replaces LSI by advances insemiconductor technologies or other derivative technologies becomesavailable, functional blocks can obviously be integrated using suchtechnologies. Application of biotechnology or the like is also apossibility.

One embodiment of the present disclosure can be not only such a paindifferentiation/estimation model generation, sustained paindifferentiation/estimation unit, but also a pain classifier generation,pain differentiation/classification method using characteristicconstituent units contained in a pain differentiation/estimationapparatus as steps. Further, one embodiment of the present disclosurecan be a computer program for implementing each characteristic step infeature contracting, feature extraction, pain differentiation/estimationmodel generation, and pain differentiation/estimation on a computer. Oneembodiment of the present disclosure can also be a computer readablenon-transient recording medium on which such a computer program isrecorded.

In each embodiment described herein, generation of a sample fordifferentiating the pain of an object may be carried out by, forexample, the following method. In other words, a method comprising: a)the step of carrying out a pain test to a plurality of subjects toobtain a plurality of COVAS data;

b) the step of averaging the plurality of COVAS data to create a COVAStemplate;c) the step of carrying out the pain test to the subjects to obtainbrainwave data or analysis data thereof from the subjects;d) the step of cutting out the brainwave data or analysis date thereofbased on the COVAS template; ande) the step of using the cut out brainwave data or analysis data thereofas data for learning and learning a value of a COVAS data correspondingto the cut out brainwave data or analysis data thereof as a label tocreate a model.

This method is characterized in that a pain test is carried outbeforehand to a plurality of subjects that are not an object, whereinthe plurality of COVAS data obtained from the pain test is averaged tocreate a COVAS template.

The pain test is a test of imposing any pain, wherein the pain isimposed on a plurality of subjects in accordance with a predeterminedprofile. The pain may be, for example, electrical stimulation, or may bethermal stimulation. The pain, for example, may be stimulation with anintensity that increases in a step-like manner from weak stimulation tostrong stimulation, may be stimulation with an intensity that decreasesin a step-like manner from strong stimulation to weak stimulation, maybe a combination thereof, or may be stimulation with an intensity thatfluctuates between weak stimulation and strong stimulation.

The COVAS (computerized visual analog scale) data expresses subjectiveevaluation of pain by a plurality of subjects when a pain test has beencarried out to the plurality of subjects. The COVAS data is associatedwith each subjective evaluation to each pain in the pain test. The COVASdata has the length of the amount of time of the pain test.

The plurality of subjects may preferably be healthy people against thepain. This means that a COVAS template expresses the subjectiveevaluation of pain by healthy people by averaging the COVAS data by aplurality of subjects.

Furthermore, this method is characterized in that the brainwave data oranalysis data thereof obtained by carrying out the pain test to anobject differentiating pain is cut out based on a COVAS template thathas been created beforehand. Herein, in the pain test, the pain isimposed on the object in accordance with the same profile as the paintest carried out for creating a COVAS template.

Upon cutting out the brainwave data or analysis data hereof based on aCOVAS template that has been created beforehand, it is preferable thattiming of initiation of pain stimulation be made consistent between aCOVAS template and brainwave data or analysis data thereof to be cutout. This enables the COVAS template to correspond to the cut outbrainwave data or analysis data thereof as a label. In other words, itbecomes possible to differentiate what kind of pain causes the brainwavedata or analysis data thereof via the subjective evaluation of the COVAStemplate. The brainwave data or analysis data thereof labeled by theCOVAS template can be used for learning for creating a model fordifferentiating pain.

The timing of initiation of pain stimulation may be able to be madeconsistent by, for example, matching a trigger showing the timing ofinitiation of pain stimulation comprised in the brainwave data oranalysis data thereof and a trigger showing the timing of initiation ofpain stimulation comprised in the COVAS template.

Furthermore, this method is characterized in that the cut out brainwavedata or analysis data thereof is used as data for learning and a valueof a COVAS template corresponding to the cut out brainwave data oranalysis date thereof is learned as a label to create a model.

The methodology used for learning may be any methodology. Themethodology used for learning may be, for example, LSTM (Long short-termmemory). For example, the cut out brainwave data or analysis datathereof is used for input of LSTM and a value of the COVAS template isused for the label thereof (supervisory output) to carry out learning.

In the step of learning, the processing of augmenting the supervisorydata used for the machine learning described above can be carried out.

It is preferable that, before augmenting the cut out brainwave data oranalysis data thereof, the COVAS template be sorted, and, in accordancetherewith, the cut out brainwave data or analysis date thereofcorresponding to the COVAS template be sorted, and the brainwave data oranalysis data thereof close to the value of the COVAS template beaugmented as a collective (e.g., unit of 5 samples). This enablesdefinition of an appropriate label for an augmented sample.

The sorting can be carried out in any order. For example, sorting may becarried out in an order of increase from lower to higher value of theCOVAS template, or sorting may be carried out in an order of decreasefrom higher to lower of the value of the COVAS template.

(Pain Classifier Generation)

In one aspect, the present disclosure provides a method of generating apain classifier for generating pain that an object being estimated hasbased on a brainwave of the object being estimated. The methodencompasses a) the step of stimulating the object being estimated with aplurality of levels stimulation intensities, b) the step of obtainingbrainwave data of the object being estimated corresponding to thestimulation intensity (also referred to as brain activity data, amountof brain activity; e.g., brainwave amplitude data (“EEG amplitude”),frequency property, or the like), c) the step of augmenting brainwavedata or analysis data thereof of the object being estimated, comprisingi) deriving a covariance matrix from brainwave data or analysis datathereof of the object being estimated, ii) decomposing the covariancematrix and iii) applying a random number to the decomposed matrix, d)the step of plotting the stimulation intensity or a subjective painsensation level corresponding to the stimulation intensity and thebrainwave data to fit to a pain function such as a linear function withthe range of inflection linearly approximated or a more comprehensivesigmoid function pattern encompassing the above to obtain a painfunction specific to the object being estimated, and e) the step of,when regression coefficient of the fitting to the specific pain functionis equal to or more than what is predetermined, identifying a painclassifier for dividing a pain level into at least two or more (strong,medium, weak and the like are also possible) based on the specific painfunction.

Alternatively, the present disclosure provides an apparatus generating aclassifier for classifying pain that an object being estimated has basedon a brainwave of the object being estimated. This apparatus comprisesA) a stimulation unit stimulating the object being estimated with aplurality of levels of stimulation intensities, B) a brainwave dataobtaining unit obtaining brainwave data (e.g., amplitude data) of theobject being estimated corresponding to the stimulation intensity, C) anaugmentation unit augmenting brainwave data or analysis data thereof ofthe object being estimated, wherein the augmentation unit is configuredto i) derive a covariance matrix from brainwave data or analysis datathereof of the object being estimated, ii) decompose the covariancematrix and iii) apply a random number to the decomposed matrix, and D) apain classifier generation unit plotting the stimulation intensity or asubjective pain sensation level corresponding to the stimulationintensity and the brainwave data to fit to a pain function such as alinear function with the range of inflection linearly approximated or amore comprehensive sigmoid function pattern encompassing the above toobtain a pain function specific to the object being estimated andidentifying a pain classifier for dividing a pain level into at leasttwo or more based on the specific pain function. Typically, step a) isperformed in the A) stimulation unit, step b) is performed in the B)brainwave data obtaining unit, the above-described processing is carriedout in view of FIGS. 3 to 5 in the C) augmentation unit, and step c) andstep d) are performed in the D) pain classifier generation unit.

In the present disclosure, “estimation” or “differentiation” can becarried out by “classification” of pain. It is understood that, whenwhether the pain is strong/weak is understood by carrying out “painclassification” operation can be carried out so as not to impose strongintensity and the action/effect of an analgesic agent such asobjectively understanding therapeutic effect would be obtained. It ispossible to estimate “strong stimulation” from “weak stimulation”, and,as long as it is possible to identify the range of change in the brainactivity feature related to weak pain, “whether or not pain that is notweak is felt” can be estimated that “increase in the frequency ofappearance of deviated feature=stronger pain”. Since there is no labelregarding the degree of pain that is strongly felt by a patient in anactual scene, it is preferable to present reference stimulation fromweak pain to about a medium degree of the inflection point and identifythe pattern of change in brain activity. It is possible to estimate thepain from the brain activity of the patient and differentiate the stateof the pain. When “the range of inflection in the brain activity featureregarding weak pain” is understood, when the frequency of deviation fromthe range increases, it can be estimated that “pain that is not weak isfelt”.

The following schematic diagram is used to describe a methodology ofpain classifier generation (FIG. 25).

In the step (S100) of stimulating the object being estimated with aplurality of levels of stimulation intensities which is step a), theobject being estimated is stimulated with a plurality of levels(strength or greatness) of stimulations (e.g., low temperaturestimulation, electrical stimulation, or the like). The number of typesof the stimulation intensities may be a number required for fitting tothe pain function, which generally needs to be, for example, at leastthree types. This number of types is not necessarily required sincefitting to the pain function is possible even with one type or two typesby combining with previously obtained information. Meanwhile, when afitting is newly carried out, it may be generally advantageous to carryout stimulation with at least 3 types, preferably four types, fivetypes, six types or more types of levels of stimulation. In this regard,since burden on the object being estimated should be as little aspossible, the stimulation intensity has high invasiveness to the objectbeing estimated (in other words, the intensity that a subject cannotbear) and it is preferable that the number thereof be minimum or zero.Meanwhile, since stimulation with high invasiveness to an object beingestimated may be required for a more accurate fitting, a minimum numbercan be taken in in accordance with the purpose. For example, the numberof types of levels with high invasiveness to an object being estimatedmay be at least one type, at least two types, or at least three types,or may be four types or more when allowed by the object being estimated.

Step b) is a stem (S200) of obtaining brainwave data of the object beingestimated corresponding to the stimulation intensity (also referred toas brainwave activity data, amount of brainwave activity, or the like;including, for example, amplitude data (“EEG augmentation”), frequencyproperty, or the like), wherein such brainwave data can be obtainedusing any methodology that is well known in the art. Brainwave data canbe obtained by measuring electrical signals of a brainwave and isdisplayed by potential (can be displayed by μV or the like) as amplitudedata or the like. Frequency properties are displayed as power spectrumdensity or the like.

In a preferred embodiment, in order to practice the present disclosure,brainwave data is preferably collected by a simple method, which can 1)use as less electrodes as possible (about two), 2) avoid the scalp withhair as much as possible, and 3) record while sleeping, to carry out theinvention. However, the number of electrodes may be increased as needed(e.g., may be three, four, five, or the like).

Step c) is a step of plotting the stimulation intensity and theaugmented brainwave data to fit to a pain function (linear function orsigmoid curve) to obtain a pain function specific to the object beingestimated (S300). In this step, the stimulation intensity used in stepa) and the data in which the brainwave data obtained in step b) has beenaugmented by the augmentation unit are used to create a plot diagram tofit to a pain function. Fitting to a pain function can be carried outusing any methodology known in the art. Specific examples of suchfitting functions include, but are not limited to, linear function, aswell as a Boltzmann function, double Boltzmann function, Hill function,logistic dose response, sigmoid Richards function, sigmoid Weibullfunction, and the like. A standard logistic function is particularlycalled a sigmoid function. A standard function or a modified formthereof is common and preferred.

Step d) is a step (S400) of, when regression coefficient of fitting tothe pain function is equal to or more than what is predetermined asneeded, identifying a pain classifier for dividing a pain level into atleast two or more (or two to three or more stages of pain levels interms of quantity/quality) based on the pain function. Identification ofa classifier can be determined, but not limited to, based on theinflection point (central value or the like) of the pain function. Asneeded, pain classifiers can be calibrated to maximize the pain levelclassification. For example, a pain classifier can provisionallydetermine brainwave data corresponding to the inflection point of thepain function as a pain classifier. This pain classifier can becalibrated so that the original brainwave data and stimulation intensitycorresponding thereto or the subjective pain sensation level of anobject corresponding to the stimulation intensity would be actuallyevaluated and that the outlier would be less, preferably minimized. Sucha pain classifier can be applied to calculation or classification ofpain level and can be used for determination of the effect of therapy.

When the same subject is the object, previous classifier data may beused to comprise the step of succeeding or updating the classifier.

In the apparatus for the pain classifier generation of the presentdisclosure, A) the stimulation unit stimulating the object beingestimated with a plurality of levels of stimulation intensities isconfigured so as to perform step a). In other words, the apparatus hasmeans or function that can provide a plurality of types of stimulationintensities. Furthermore, the apparatus is configured so that suchstimulation can be imposed on an object.

B) the brainwave data obtaining unit obtaining brainwave data (e.g.,amplitude data) or analysis date thereof of the object being estimatedcorresponding to the stimulation intensity is configured to obtainbrainwave data or analysis data thereof of an object being estimated.The brainwave data obtaining unit performs step b) and may also haveother functions (e.g., step e) in a classification apparatus).

C) the pain classifier generation unit plotting the stimulationintensity or subjective pain sensation level corresponding to thestimulation intensity and brainwave data augmented by the augmentationunit to fit to a pain function such as a linear function with the rangeof inflection linearly approximated or a more comprehensive sigmoidfunction pattern encompassing the above to obtain a pain functionspecific to the object being estimated and identifying a pain classifierfor dividing a pain level into at least two or more based on thespecific pain function may have the function of carrying out fitting ofthe calculated specific pain function and generation of a painclassifier. C) the pain classifier generation unit normally performsstep c) and step d). These two functions may be materialized in separateapparatuses, devices, CPUs, terminals, or the like, or may bematerialized as one part. One CPU or calculation apparatus is normallyconfigured to incorporate or be able to incorporate a programmaterializing these calculations.

FIG. 26 describes a schematic diagram of the apparatus of the presentdisclosure. Among the embodiments, this embodiment is a pain classifiermeasurement apparatus, thereby involving 1000 to 3000. A stimulationunit 1000 corresponds to A), wherein a value of stimulation iscommunicated to a brainwave data obtaining unit 2000 and pain classifiergeneration unit 3000. The brainwave data obtaining unit 2000 isconfigured (2500) so as to comprise or be connected with anelectroencephalograph that is or can be connected to the object (1500),so that brainwave data or analysis data thereof is obtained fromstimulation emitted from the reference stimulation application unit tothe object (1500). Sample augmentation can be applied to the dataobtained at the brainwave data obtaining unit. The sample augmentationmay be applied in the electroencephalograph 2500, or may be applied to adifferent unit such as a pain classifier generation unit 3000 or a painclassifier unit 4000, or a sample augmentation unit (not shown) may beindependently provided.

FIG. 27 is a block diagram showing the functional configuration of painestimation or pain classification or pain classifier generation system5100 of one embodiment (it should be noted that parts of thisconfiguration diagram are optional configuration parts which may beomitted). The system 5100 comprises a brainwave measurement unit 5200,which internally comprises or externally connects to a brainwaverecording sensor 5250 and optionally a brainwave augmentation unit 5270,and processes signals of a pain related brainwave anddifferentiates/estimates pain in the pain differentiation/estimationapparatus unit 5300. The pain differentiation/estimation apparatus unit5300 carries out processing of a brainwave signal in a brainwave signalprocessing using 5400, (if necessary, extracts brainwave feature in abrainwave feature extraction unit 5500), estimates/differentiates painin the pain differentiation/estimation unit 5600, and (as needed)visualize pain in a pain level visualization unit 5800. In addition, astimulation apparatus unit 5900 is comprised inside or outside, whereinthis stimulation apparatus unit 5900 comprises a reference stimulationapplication unit (terminal) 5920, which contributes to creation of apain classification tool of a patient. The stimulation apparatus unitmay, as needed, comprise a reference stimulation level visualizationunit 5960 also comprising a reference stimulation generation unit 5940.

Accordingly, the pain classifier generation system 5100 comprises abrainwave measurement unit 5200 and a pain differentiation/estimationapparatus unit estimation unit 5300, and comprises a stimulationapparatus unit 5900 (may comprise a reference stimulation unit) asneeded. The pain differentiation/estimation apparatus unit 5300 is, forexample, materialized by a computer comprising a processor and a memory.In this case, when a program stored in the memory is performed by theprocessor, the pain differentiation/estimation apparatus unit estimationunit 5300 causes the processor to function as a brainwave augmentationunit 5270 as needed, brainwave signal processing unit 5400, paindifferentiation/estimation unit 5600 (as needed), pain levelvisualization unit 5800 (as needed) and the like. The brainwaveaugmentation unit 5270 can augment the signal intensity of a brainwave.Reference stimulation and visualization are also caused as needed. Inaddition, the system 5100 or apparatus unit 5300 of the presentdisclosure may be materialized by, for example, a dedicated electricalcircuit. A dedicated electrical circuit can be a single integratedcircuit or a plurality of electrical circuits. The brainwave dataobtaining unit and pain classifier generation unit may have the sameconfiguration as this pain estimation apparatus. The sample augmentationcan be applied to data obtained in the brainwave measurement unit 200.Sample augmentation may be applied in the brainwave measurement unit5200 (e.g., brainwave augmentation unit 5270), or may be applied to adifferent configuration unit in the pain differentiation/estimationapparatus 5300, or a sample augmentation unit (not shown) may beindependently provided.

The brainwave measurement unit 5200 obtains a plurality of brainwavedata by carrying out a plurality of times of brainwave measurement froman object being estimated via an electroencephalograph (brainwaverecording sensor 5250). An object being estimated is an organism inwhich change in the brainwave is caused by pain, which does not have tobe limited to people.

The pain differentiation/estimation unit 5600 generates pain classifier.The pain classifier is for estimating or classifying the greatness ofpain from the amplitude of plurality of brainwave data. In other words,the pain differentiation/estimation unit 5600 can generate painclassifier for estimation of classification of pain of the object fromthe brainwave data.

The brainwave recording sensor 5250 measures the electrical activitygenerated in the brain of an object being estimated with an electrode onthe scalp. Furthermore, the brainwave recording sensor 5250 outputsbrainwave data which is the result of measurement. The brainwave datacan be augmented as needed.

Next, a processing or method of an apparatus with the above-describedconfiguration is explained. FIG. 25 is a flow chart showing a series ofprocessing. In this aspect, S100 to S400 are involved. A pain classifier(also referred to as pain classification tool/plain prediction tool) isgenerated in S400.

Stimulation with a plurality of levels (greatness) of stimulationintensities are imposed on an object through the reference stimulationunit 1000 (S100).

Next, brainwave data (brainwave amplitude standard data such asamplitude data) is obtained (S200). Obtainment of brainwave data iscarried out by the brainwave data obtaining unit 2000 according to FIG.26. According to FIG. 27, the brainwave measurement unit 5200 obtains aplurality of brainwave data from an object being estimated via anelectroencephalograph (brainwave recording sensor 5250) by carrying outa plurality of times of brainwave measurement to achieve brainwave data(e.g., amplitude data). The brainwave measurement unit 5200 may carryout brainwave measurement at a plurality of times. The brainwave datamay be augmented by the above-described system 100.

The pain classifier generation unit 3000 (see FIG. 26) carries out painfunction fitting (S300). When pain function fitting is carried out andit is determined that the regression coefficient is an appropriate valueas needed, this pain function can be used to generate a pain classifier(pain classification tool/pain prediction tool) (S400). After generatinga pain classifier, calibration can be carried out as needed.

In each of the embodiments described above, each constituent element canbe materialized by being configured with a dedicated hardware or byimplementing software program that is suited to each constituentelement. Each constituent element can be materialized by a programimplementation unit such as a CPU or a processor reading out andimplementing a software program recorded on a recording medium such as ahard disk or semiconductor memory. In this regard, softwarematerializing the pain estimation apparatus of each of the embodimentsdescribed above or the like can be a program such as those describedbelow.

As used herein, “or” is used when “at least one or more” of the listedmatters in the sentence can be employed. When explicitly describedherein as “within the range of two values”, the range also includes thetwo values themselves.

Reference literatures such as scientific literatures, patents, andpatent applications cited herein are incorporated herein by reference tothe same extent that the entirety of each document is specificallydescribed.

As described above, the present disclosure has been described whileshowing preferred embodiments to facilitate understanding. The presentdisclosure is described hereinafter based on Examples. The abovedescriptions and the following Examples are not provided to limit thepresent disclosure, but for the sole purpose of exemplification. Thus,the scope of the present disclosure is not limited to the embodiments orthe Examples specifically described herein and is limited only by thescope of claims.

EXAMPLES

Examples are described hereinafter. The objects used in the followingExamples were handled, as needed, in compliance with the standards ofthe Osaka University, and the Declaration of Helsinki and ICH-GCP inrelation to clinical studies.

Example 1: Augmentation of Pain Analysis Result=Closed Eye SampleAugmentation

In this example, a closed eye sample was used to carry out painanalysis. Upon doing so, sample augmentation was carried out.

(Method and Material)

(Closed Eye Sample)

A closed eye sample refers to reaction data against stimulation of whenthe eyes of a subject are closed. In this example, in the eye-closingtask of having the subject close the eyes, reaction data, which isbrainwave data herein, in four different classes, “no pain”, “havingpain”, “no pain with noise”, “having pain with noise”, was obtained. “Nopain” shows a stable state with no stimulation, “having pain” shows astate when having 48° C. of thermal stimulation, “no pain with noise”shows a state of when noise upon movement such as tightly closing theeyes, stretching the body, or reading out loud is inputted, and “havingpain with noise” shows the state of when noise associated with movementof the body upon 48° C. of thermal stimulation is inputted. A subjectwas asked to create each of the four states to obtain brainwave datathereupon.

The experimental trial is as described below.

(1) artifact1: noise test (tightly closing the eyes, stretching thebody, reading out loud), eyes opened(2) artifact2: noise test (tightly closing the eyes, stretching thebody, reading out loud), eyes opened(3) artifact_pain1: noise test upon pain stimulation (voluntary reactionwith noise inputted), eyes opened(4) artifact_pain2: noise test upon pain stimulation (voluntary reactionwith noise inputted), eyes opened(5) ref: pain stimulation, stable, eyes closed(6) main1: pain stimulation, stable, eyes closed(7) main2: pain stimulation, stable, eyes closed(8) main3: noise test upon pain stimulation, eyes closed(9) 2temp: pain stimulation (moderate: 46° C., great: 48° C.), eyesopened(10) 2temp_artifact: noise test upon pain stimulation (moderate: 46° C.,great: 48° C.) (voluntary reaction with noise inputted), eyes opened

The brainwave uses 6ch of the forehead to extract the frequency powerfrom the absolute amplitude and six frequency bands (2-5 Hz, 5-8 Hz,8-14 Hz, 14-29 Hz, 31-40 Hz, 40-49 Hz) as a feature.

As pre-processing, EOG removal and bandpass filter were applied.

Data was collected while being divided into data for model creation anddata for test (actual performance). (2), (4) and (5) of theexperimentation trial are for creating a model, and (1), (3) and (6) to(10) are for test. Brainwave data of each class, no pain with no noise,having pain with no noise, no pain with noise and having pain withnoise, was cut out using the time window of 8 seconds. A plurality oforiginal samples were generated by shifting the time window in thedirection of the time axis and carrying out cutout a plurality of times.

Sample augmentation method is applied to the plurality of originalsamples for each individual and to each of four classes to create amodel to be fitted to the individual using LSTM (Long short-termmemory).

FIG. 28 shows the flow of 4 class LSTM analysis. FIG. 29 shows theanalysis condition for the 4 class LSTM analysis.

15 sequences were obtained from an 8 second time window by cutting 1second sequences with a 0.5 second overlap. Since there are 147 originalfeatures, the overall features would be 147×15. The sample augmentationwas carried out after feature extraction and before creation of a model.The result was outputted in 4 classes (“0: no pain/1: having pain/2: nopain with noise/3: having pain with noise”) and 2 classes (“0: nopain/1: having pain”) of softmax functions with respect to the off-linechronological data. The evaluation standards (differentiation precision,relevance ratio, recall ratio, F1 value) are compared in the 2 classesand 4 classes.

(Off-Line Chronological Data Analysis)

Off-line chronological data analysis was carried out in the followingviewpoints.

(I) Differentiation Value (Softmax: 4 Classes)

A value differentiating which of the 4 classes is the one. The outputvalues are, 0: no pain with no noise, 1: having pain with no noise, 2:no pain with noise, and 3: having pain with noise.

The results of all binding layers are inputted to the softmax functionand the class with the highest percentage was determined as thedifferentiation value.

(II) Differentiation Value (Softmax: 2 Classes)

A value differentiating which of the 2 classes is the one. The outputvalues are, 0: no pain and 1: having pain.

The result in the 4 classes of (1) was converted to the 2 classes.Specifically, the differentiation values 0 and 2 of the 4 classes wereconverted to the differentiation value 0 of the 2 classes and thedifferentiation values 1 and 3 of the 4 classes were converted into thedifferentiation value 1 of the 2 classes.

In this example, the differentiation values of these 2 classes and thecorrect label (where there is thermal stimulation) are compared to carryout the evaluation (differentiation precision, relevance ratio, recallratio, F1 value).

The following 8 trials have the correct label of thermal stimulation:

(III) artifact_pain1, (4) artifact_pain2, (5) ref, (6) main1, (7) main2,(8) main3, (9) 2temp, (10) 2temp_artifact.(3) Pain estimation value: −log(1−x)

The pain estimation value (0-1) is a value converted with −log(1−x).When setting a threshold close to 1 (e.g., 0.99), the fluctuation in theestimated value is easier to see.

(IV) Brainwave: Fp1

(V) Feature: There are 147×15 features, wherein 147 features and 15chronological sequences form a unit.

(Result)

FIG. 30(A) to FIG. 30(T) show the raw data obtained in this example, orthe off-line chronological analysis result of the raw data. Among theoff-line chronological analysis result of the raw data, the first datafrom the top and the second data from the top are important, wherein thefirst data is a result of the softmax function of 4 classes (“0: nopain/i: having pain/2: no pain with noise/3: having pain with noise”)and the second data is the result of the softmax function of 2 classes(“0: no pain/1: having pain”). Each show to which class the featurecalculated from the brainwave will be classified in the chronologicaldata.

The data for test (actual performance) may correspond to unknown data,and modification of a model so as to obtain high differentiationprecision upon testing (actual performance) is the problem to be solvedof machine learning. For example, in (6) main1, (7) main2 and (8) main3,when it is differentiated as “having pain” when “having pain” with 48°C. of thermal stimulation and it is differentiated that there is “nopain” when there is “no pain” with no stimulation, it can be judged thatthe model is a good model.

Furthermore, since a noise that is not possible to be differentiated bythe two-value classification of “having pain/no pain” may be inputted inthe brainwave, the classification of the 4 classes also enabledclassification of “noise” in consideration of such a case.

Example 2: Augmentation of MRI Analysis Result

In this example, the effect of sample augmentation was confirmed in theMRI analysis result. Sample augmentation regression was carried out.

(Method and Material)

1.1 Animal

Wild-type mice are used to be divided into the groups, Native group,Model day2 group and Model day28 group, to carry out comparison for eachgroup. A group with nothing done was referred to as native, a group 28days after operation was referred to as day28, a group in which 10 mg ofgabapentin was administered to day28 was referred to as day28+gaba10,and a group in which 100 mg of gabapentin was administered to day28 wasreferred to as day28+gaba100.

1.2 Schedule

The administration schedule is as described below. Two days prior to theday of imaging, 15 mg/kg of manganese was intravenously administered fortwo days. Regarding agent administration groups, evaluation of 2 groupswith different dosages of gabapentine which are 100 mg/kg and 10 mg/kgwas attempted, wherein each underwent 2 times/day intraperitonealadministration for 2 days.

1.3 MRI

MRI measurement uses the 11.7T-MR scanner in the CiNet ward of OsakaUniversity to carry out imaging of a T1 emphasized image and a T2emphasized image using a spin echo sequence under isoflurane anesthesia.The imaging region was the entire brain. The MRI imaging parameter is asdescribed below.

TABLE 1 Type of image, Item, Condition T2 enhanced image, Slices, 34 T1enhanced image, Slices, 10 T2-T1 common, Slice Orient, Axial T2-T1common, Slice Thickness, 0.50 mm T2-T1 common, FOV Read, 2.00 mm T2-T1common, FOV P1, 2.0 mm

In order to carry out imaging of the entire brain with a T1 enhancedimage, the imaging needs to be divided into 4 times. In order to preventlack of slice, imaging was carried out in a manner in which two piecesoverlap. Overlap of the T1 enhanced image divided into 4 was deleted tointegrate into one image using a SanteDICOM software.

1.4 Correction of Image

Imaging of the T1 enhanced image is carried out while being divided into4, wherein, since the brightness would greatly differ at the cut part ofthe image, direct use for analysis is not possible. Thus, an imageprocessing software ImageJ is used to extract the muscle layers (7sites) in each slice and brain brightness to correct the brightness.

1.5 Standardization Processing of Image

There are differences in the shape and size of the brain amongindividuals. Thus, standardization processing of the T1 enhanced imageof an individual that underwent imaging was carried out. Thestandardization processing was carried out using MatLab.

1.5.1 Positioning of T2 Enhanced Image and T1 Enhanced Image

The T1 enhanced image was designated as the Reference Image and the T2enhanced image was designated as the Source Image to carry outpositioning by linear correlation.

1.5.2 Positioning of T2 Enhanced Image and Standard Template of T1Enhanced Image

A T2 standard template is designated as the Reference Image, a T2enhanced image is designated as the Source Image, and a T1 enhancedimage is designated as the Other image to carry out positioning bylinear correlation.

1.5.3 Masking

A brain site extraction image of a T2 standard template and a T2enhanced image (or T1 enhanced image) positioned with a template aredesignated as the Input Image, an output file name is inputted to theOutput Filename, and the output folder is designated at the OutputDirectory to carry out extraction of the brain site.

1.5.4 Positioning of a Nonlinear Shape (Normalize)

A masked T2 enhanced image is designated as the Source Image and maskedT2 enhanced image, T1 enhanced image and T2 enhanced image and T1enhanced image positioned with a template are designated as the Imagesto Write to carry out standardization processing.

1.6 VOI Analysis of Standardization Data

VOI analysis was carried out using an in-house software after carryingout brightness correction again in the standardized T1 enhanced image.Brain map information uses a mouse template by Mirrione.

(Sample Augmentation)

Labeling was carried out while setting the pain score of native (N=7) as0 and setting day28 (N=8) as 100. Data of native (N=7) and day28 (N=8)having 19 features (brain regions) are pooled to carry outstandardization (forming z value using a mean value and a standarddeviation: data=(data−mu)/sigma). By carrying out sample augmentation tothese data, the N count of native and day28 is increased up to 100,1000, 4000, 10000 and 20000, respectively, which was repeated 100 timesat a time regarding each augmentation count (creating training data).

(Creation of a Model)

Each of day28+gaba100 (N=5) and day28+gaba10 (N=5) is standardized witha standardization parameter that has been preserved (test data wasprepared). Next, each augmented sample (training data) [100, 1000, 5000,10000, 20000] is used to create 100 regression models with SVR.

Each created model is used to carry out fitting regarding day28+gaba100(N=5) and day28+gaba10 (N=5) to calculate the mean value. In this case,it can be understood that the result of regression is a pain score, andthe group is a group wherein the larger the value, the stronger the painis felt.

FIG. 31 shows the entire image of the analysis of this example. FIG. 32shows an image of the stage of augmenting data of naïve (N=7) and day28(N=8). The data of native (N=7) and the data of day28 (N=8) are sued toaugment each group up to 100, 1000, 5000, 10000 and 20000 by the sampleaugmentation of the present disclosure to create a model using theaugmented data.

FIG. 33 shows an image of the stage of carrying out fitting using acreated model and predicting the pain score. Creation of a regressionmodel using SUV is repeated 100 times in each augmented sample 100,1000, 5000, 10000, 20000, wherein each model is used to carry outfitting regarding day28+gaba100 and day28+gaba10 to calculate a meanvalue.

(Result)

FIG. 34 shows the result of the present example.

In the augmented sample 100, the mean value of the pain score ofday28+gaba100 (N=5) was 59.96, and the mean value of the pain score ofday28+gaba10 (N=5) was 69.88. In the augmented sample 1000, the meanvalue of the pain score of day28+gaba100 (N=5) was 52.41, and the meanvalue of the pain score of day28+gaba10 (N=5) was 75.81. In theaugmented sample 5000, the mean value of the pain score of day28+gaba100(N=5) was 39.31, and the mean value of the pain score of day28+gaba10(N=5) was 73.55. In the augmented sample 10000, the mean value of thepain score of day28+gaba100 (N=5) was 27.70, and the mean value of thepain score of day28+gaba10 (N=5) was 74.14. In the augmented sample20000, the mean value of the pain score of day28+gaba100 (N=5) was26.09, and the mean value of the pain score of day28+gaba10 (N=5) was72.69.

(Observation)

The greater the number of augmentation samples, the more significantlythe difference in the pain scores of day28+gaba100 and day28+gaba10appeared. For example, in the case of N=20000, the pain score was 26.09in day28+gaba100, whereas the pain score was 72.69 in day28+gaba10. Thissuggests that the group administered with 100 mg of gabapentin hasgreater pain relief effect compared to the group administered with 10 mgof gabapentin.

Example 3: Augmentation of miRNA Analysis Result

In the present example, the effect of sample augmentation was confirmedin the miRNA analysis result. Specifically, comparison of sampleaugmentation method for excluding outliers was carried out.

(Method and Material)

The extraction procedure of miRNA is as described below.

I. Mouse brain (about 0.4 to 0.45 g per mouse brain) was collected.II. The mouse brain is crushed and the RNA was extracted.III. Small RNA Library construction, cDNA purification and sizeselection (using Ion Total RNA-Seq Kit v2) were carried out.IV. cDNA was augmented.V. The generated cDNA is used to carry out gene analysis.

Each step is explained below.

I. Mouse Brain Collection

1. Numbers were written to 5 mL tubes (QSP, 580-GRDS-Q) of RNase, DNase,& Pyrogen Free, 4 to 4.5 mL of RNAlater was added and the weight of eachtube was measured and written in a table. A brain sample was immersed infive times the amount of RNAlater (about 04 to 0.45 g per mouse brain).

2. After blood sample collection, a mouse is perfused with 50 mL ofPBS(−) to wash away the red blood cells in the tissue.

3. The entire brain is taken out and immediately put into a 5 mL tubewith RNAlater therein. An iron was used to cut the brain sample so thatthe length of any piece would be 0.5 cm or less (e.g., 0.5 cm×1 cm×1cm). The lid was tightly closed and the weight of each tube is measuredagain to sort out the weight of the brain sample. When there is notenough RNAlater, a necessary amount was added to the tube. The samplewas preserved at 4.4° C. (the sample was preserved at 4° C. if it iswithin one month, and when preserved for a longer period, the sample waspreserved at −20° C.).

II. Crushing of Mouse Brain and Extraction of RNA

(A commercially available RNeasy Lipid Tissue Mini Kit was used as theQIAzol reagent, and a commercially available mirVana Isolation Kit wasused for extraction of miRNA)

1. Sterilized zirconia beads (5Φ) were put into sterilization tubes forTomy crushing one by one with sterilized tweezers. 1 mL of QIAzol LysisReagent was dispensed into each tube.

2. One whole brain is taken out from RNALater with sterilized tweezersand RNAlater was wiped off from the brain sample while being pressedwith a Kimwipe.

3. The entire brain from which RNAlater was wiped off was divided intofour and evenly placed into four tubes with zirconia beads and QIAzolLysis Reagent therein.

4. A tube for crushing with ¼ of the brain therein was crushed one timeat 4,100 rpm×30 seconds. After waiting for one minute (cooling),crushing was carried out again at 4,100 rpm×30 seconds and all solutionsamples were collected into one 5 mL tube. (When foam was made,especially the brain tissue was left for 2 to 3 minutes at roomtemperature.) When the 30 second crushing is repeated for 2 to 3 times,the brain tissue inside the tube is substantially crushed into smallpieces.

5.1 200 μL was dispensed for miRNA (using a mirVana lit) from brainsamples collected into one and 800 μL of Lysis/binding buffer was addedto mix at a pipette tip. 1/10 capacity (100 μL) of miRNA Homogenateadditive was added, Vortex was carried out, and equal division into two1.5 mL tubes was carried out to perform On ice×10 min.

6. The remaining brain sample was divided into 3, wherein one (1 mL) ofthem is added to a 2 mL tube for total RNA to be left for 5 minutes atroom temperature (RT). (Total RNA uses a Qiagen RNeasy Lipid tissuekit.) The remaining two was frozen and preserved at −80° C. Total RNAwas extracted in accordance with the protocol of the kit.

7. Acid-phenol: chloroform was added in the amount of 1:1 to the brainsample for microRNA. (1:1 with respect to the amount of lysate w/outmiRNA homogenate additive)→30 to 60 seconds of Vortex for mixing.

8. A microRNA tube underwent centrifugation in the conditions, 10,000×g,RT and 5 minutes. Since it is divided into three layers, the top layer,aqueous phase, was moved to a new tube. The amount (Vol.) of the aqueousphase that had been moved to the tube was written down and was On iced.

9. ⅓ of 99.5% EtOH was added to the aqueous phase of mirVana that was Oniced. Vortex was carried out to be mixed well.

10. Filter cartridge was put into a collection tube and the Lysate-EtOHmixed in 21 was added to 700 μL of filter cartridge for centrifugation(10,000×g, RT, 15 seconds). The filtrate was collected. This wasrepeated until filtration/centrifugation of all Lysate-EtOH iscompleted.

11. ⅔ of 99.5% EtOH is added to the collected filtrate and Vortex wascarried out to be mixed well.

12. A new filter cartridge and collection tube are taken out to add 700μL of filtrate-⅔ EtOH mixing liquid for centrifugal separation(10,000×g, RT, 15 seconds). The flow through is thrown away. Allfiltrate-⅔ EtOH was repeated until completion of filtration/centrifugalseparation. The flow through was thrown away.

13. 700 μL of miRNA wash Soln 1 was added to the filter for washing.Centrifugal separation (10,000×g, RT, 15 seconds) was carried out tothrow out the flow through.

14. 500 μL of Wash Soln ⅔ was added to the filter for washing.Centrifugal separation (10,000×g, RT, 1 minute) was carried out. Thiswas repeated 2 times and the flow through was thrown away.

15. When second washing is completed, the flow through is thrown awayand centrifugal separation was carried out again (10,000×g, RT, 1minute).

16. The filter cartridge is put into a new collection tube and theElution Soln. that had been warmed at 95° C. was added with a 100μL/tube for centrifugal separation (10,000×g, RT, 30 seconds).

17. 10 μL is dispensed into a 500 μL tube for Bioanalyzer to measure thepurity with the Bioanalyzer. The rest was preserved at −30° C. (theBioanalyzer studies the purity of the extracted miRNA).

18. If the Bioanalyzer did not find any issue regarding the sample, thesample was moved to the small RNA library construct.

III. Construct of Small RNA Library, Purification of cDNA and SizeSelection (Using Ion Total RNA-Seq Kit v2) were Carried Out.

(Operations Other than Defrosting were Carried Out on Ice.)

1. A necessary reagent and an miRNA sample was moved from 20° C. to afridge (4° C.) for defrosting.

2. A reagent for hybridization was prepared on ice. (Total Vol=5μL/sample) Hybridization solution (3 μL/sample) 3 μL×Ion Adaptro Mix v2(2 μL/sample) 5 μL of the above-described mixed reagent was dispensedinto a 2 μL×3.0.2 mL tube and 3 μL of miRNA sample was added to mix withtapping or pipetting for a light spin-down to collect the reactionliquid at the bottom of the tube. A defrosted miRNA sample lightlyunderwent Vortex before use to drop a droplet with a tabletopcentrifuge.

4. Hybridization reaction is carried out. (65° C.×10 minutes, 16° C.×5minutes)

*During the above, a reagent for ligation was prepared on ice (extraregion is regulated in the consideration of pipetting loss) Vortex iscarried out for a light spin-down.

2× Ligation Buffer (10 μL/sample) 10 μL× Ligation Enzyme Mix (2μL/sample) 2 μL×

5. The regulated reagent for ligation is added 12 μL at a time to a 8 μLtube after hybridization reaction (the entire amount became 20 μL).

Mixing is carried out by tapping or pipetting for light spin-down tocollect the reaction liquid at the bottom of the tube.

6. 20 μL of ligation reaction liquid was reacted for 16 hours at 16° C.(Overnight)

7. Before termination of a 16-hour ligation reaction, a region forreverse transcription is prepared on ice (extra region is regulated inthe consideration of pipetting loss; Total vol.=16 μL/sample)

Nuclease-Free Water (2 μL/sample) 2 μL×10×RT

Buffer (4 μL/sample) 4 μL×2.5 mM

dNTP Mix (2 μL/sample) 2 μL× Ion

RT Primer v2 (8 μL/sample) 8 μL×

8. The region for reverse transcription is added to the ligationreaction liquid 16 μL at a time, which is mixed 5 times with pipettingfor light spin-down to collect the reaction liquid at the bottom of thetube.

9. Reaction under the condition of 70° C.×10 minutes is carried out in astate in which heat cover is set, which is immediately placed on ice.

10. 10× superscript III enzyme Mix is added to each tube(above-described tube) stored on ice 4 μL at a time, which is gentlymixed with Vortex for a light spin-down.

11. reverse transcription was initiated with a thermal cycler to which aheat cover has been set (42° C.×30 minutes). *This achievestranscription of miRNA→cDNA

*The operation can be stopped at this point (cDNA with completed reversetranscription reaction was able to be frozen for preservation. 2 weeksat −20 to −30° C., −80° C.=long term preservation)

12. Before starting purification of cDNA and size selection, a requiredreagent was changed back into room temperature (RT) (e.g., Nucleic AcidBinding Beads & Nuclease-Free Water). Ethanol is added to the Washsolution Concentrate and a check mark was written on the lid.

Necessary amount of Nuclease-Free water was kept warm at 37° C.

13. The Nucleic Acid Binding tube gently undergoes Vortex to completelydissolve the Magnetic Beads. Beads of each sample were prepared (7μL/sample/1.5 mL DNA LoBind tube). Binding solution concentrate is addedto the tube with beads 140 μL at a time and pipetting was repeated 10times to mix the beads and the Binding solution concentrate.

14. 40 μL of the cDNA sample with completed reverse transcriptionreaction is added to a 1.5 mL tube with beads therein. 120 μL of 100%ethanol (may be a 99.5% ethanol) was dispensed in each 1.5 mL tube withcDNA and beads therein. Each tube gently undergoes Vortex with thesetting of 4 for a light spin-down. In order to prevent liquid leakagefrom the tip, ethanol undergoes pipetting 3 times to dampen the insideof the tip to then be dispensed.

15. Since a large cDNA molecule would be absorbed by the beads, the tubewas left at RT for 5 minutes.

16. Each tube with cDNA and beads therein gently undergoes Vortex for alight spin-down to then be set at a Magnetic Stand to be left for 5minutes. Once the supernatant became clear, the supernatant is separatedfrom the beads using a pipette while leaving the tube at the MagneticStand and the supernatant is moved to a new 1.5 mL DNA LoBind tube (atube with beads therein was disposed).

17. 72 μL of Nuclease-Free Water was added to a 1.5 mL tube withsupernatant therein. 78 μL of 100% ethanol was added thereto. In orderto prevent liquid leakage from the tip, ethanol underwent pipetting 3times to dampen the inside of the tip to then be dispensed.

18. A Nucleic Acid binding beads tube gently underwent Vortex tocompletely dissolve the Magnetic beads. 7 μL of beads was added to a 1.5mL tube with supernatant and ethanol therein. The tube gently undergoesVortex with the setting of 4 for a light spin-down to be left for 5minutes at RT as it is.

19. The above-described tube undergoes another light spin-down to be setat the Magnetic Stand. When the supernatant became clear and separatedfrom the beads, the supernatant was removed from the tube while beingset at the Magnetic Stand (the supernatant was thrown away).

20. 150 μL of Wash Solution Concentrate is added to the beads remainingwithin the tube set at the Magnetic Stand, which is left for 30 seconds.A pipette is set to 160 μL to cautiously remove the supernatant. Theremaining solution was removed with P10 or P20 (the supernatant wasthrown away). The tube was left with the lid open for 1 to 2 minutes toget rid of ethanol.

21. The 1.5 mL tube with beads therein was taken out from the MagneticStand. 12 μL of Nuclease-Free Water that had been kept at 37° C. wasadded to each tube to carry out pipetting 10 times for mixture. The tubewas left at room temperature for 1 minute.

22. The tube is set at the Magnetic Stand to wait for 1 minute. When thesupernatant became clear and separated from the beads, 12 μL of cDNAelution was collected in a new 500 μL tube.

IV. Augmentation of cDNA (Augmentation Using PCR and Barcode Label)

*Before initiating augmentation, a reagent is defrosted in a fridge [4°C.], undergoes light centrifugal separation at max speed, and then used.

1. A PCR reagent for augmenting a cDNA sample using a barcode wasprepared (Total=46 μL/sample).

Plutinum PCR SuperMix High Fidelity (45 μL/sample) 45 μL×

Ion Ixpress RNA 3′ Barcoded Primer (1 μL/sample) 1 μL×

2. 6 μL of cDNA sample was added to a new PCR tube. 46 μL of PCR reagentwas added thereto. Finally, 1 μL of selected Ion Xpress RNA-Seq BarcodeBC Primer is added to each PCR tube. The lid of the tube was closed toundergo a light Vortex in the setting of 4 to drop the solution at thebottom of the tube with a tabletop centrifuge.

3. cDNA augmentation reaction was carried out with a thermal cycler.After completion of the reaction, the cDNA was preserved at 4° C.

Hold: 94° C.×2 minutes

Cycle (2): 94° C.×30 seconds, 50° C.×30 seconds, 68° C.×30 seconds,

Cycle (14): 94° C.×30 seconds, 62° C.×30 seconds, 68° C.×30 seconds,

Hold: 68° C.×5 minutes

4. Reagents were prepared before carrying out purification of cDNA withaugmentation completed and size selection. A necessary amount ofNuclease-Free water was kept warm at 37° C.

5. The Nucleic Acid Binding tube gently underwent Vortex to completelydissolve the Magnetic Beads. Beads of each sample were prepared (7μL/sample/1.5 mL DNA LoBind tube). The Binding solution concentrate wasadded to a tube with beads therein 140 μL at a time to gently undergoVortex at the setting of 4 to mix the beads and the Binding solutionconcentrate.

6. 53 μL of cDNA sample augmented with PCR was added to a 1.5 mL tubewith beads therein. 110 μL of 100% ethanol (may be 99.5% ethanol) wasdispensed in each 1.5 mL tube with cDNA sample and beads therein. Eachtube gently underwent Vortex at the setting of 4 for a light spin-down.In order to prevent liquid leakage from the tip, ethanol underwentpipetting 3 times to dampen the inside of the tip to then be dispensed.

7. Since a large cDNA molecule would be absorbed by the beads, the tubewas left at room temperature (RT) for 5 minutes.

8. Each tube with cDNA and beads therein gently undergoes Vortex for alight spin-down to then be set at a Magnetic Stand to be left for 5minutes. Once the supernatant became clear, the supernatant is separatedfrom the beads using a pipette while leaving the tube at the MagneticStand and the supernatant is moved to a new 1.5 mL DNA LoBind tube (atube with beads therein was disposed).

9. A 1.5 mL tube with supernatant therein was taken out from theMagnetic Strand to add 35 μL of Nuclease-Free Water. 35 μL of 100%ethanol was added thereto. In order to prevent liquid leakage from thetip, ethanol underwent pipetting 3 times to dampen the inside of the tipto then be dispensed.

10. A Nucleic Acid binding beads tube gently underwent Vortex tocompletely dissolve the Magnetic beads. In order to absorb miRNAfractions with the beads, 7 μL of beads was added to a 1.5 mL tube withsupernatant and ethanol therein. The tube gently undergoes Vortex withthe setting of 4 for a light spin-down to be left for 5 minutes at RT asit is.

11. The above-described tube undergoes another light spin-down to be setat the Magnetic Stand. When the supernatant became clear and separatedfrom the beads, the supernatant was removed from the tube while beingset at the Magnetic Stand (the supernatant was thrown away).

12. 150 μL of Wash Solution Concentrate is added to the beads remainingwithin the tube set at the Magnetic Stand, which is left for 30 seconds.A pipette is set to 160 μL to cautiously remove the supernatant. Theremaining solution was removed with P10 or P20 (the supernatant wasthrown away). The tube was left with the lid open for 1 to 2 minutes toget rid of ethanol.

13. The 1.5 mL tube with beads therein was taken out from the MagneticStand. 15 μL of Nuclease-Free Water that had been kept at 37° C. wasadded to each tube to carry out pipetting 10 times for mixture. The tubewas left at room temperature for 1 minute.

14. The tube is set at the Magnetic Stand to wait for 1 minute. When thesupernatant became clear and separated from the beads, 15 μL of cDNAelution corresponding to miRNA was collected in a new 500 μL tube.

15. The quality and quantity of the augmented cDNA was confirmed by theBioanalyzer.

V. The Generated cDNA is Used to Carry Out Gene Analysis.

Through these procedures, all miRNA data has been prepared.

CCR2 gene KO mice were employed as the type of the mice to study themiRNA under three conditions, native (group with nothing done thereto),POD2 (group two days after the operation) and POD28 (group 28 days afterthe operation). The native was defined as C0, POD2 was defined as C1,and POD28 was defined as C2. From the three conditions, a pair of 2groups was made, wherein application of a sample augmentation method wasconsidered in each group. Specifically, native and POD2 were set asC0C1, native and POD28 were set as C0C2, and POD2 and POD28 were set asC1C2.

(Sample Augmentation)

In this example, the detection results of outliers by the followingsample augmentation methods are compared.

1. Sample augmentation method (OLD)2. Sample augmentation method (PCA)

Furthermore, (OLD) of 1. means that the method is older compared to 2.(PCA), which does not mean that it is a conventional technique.

(Sample Augmentation Method (OLD))

FIG. 35A to FIG. 35D show a schematic diagram of an analysis methodusing the sample augmentation method (OLD).

In the sample augmentation method (OLD), as shown in FIG. 35B, in thepermutation test of leave-one-out (n−1) data, only the miRNA that becamesignificant is used as a feature (feature selection).

As shown in FIG. 35C, in the case of 2 class classification, for eachclass, a mean value and standard deviation of each feature that becamesignificant for n times are calculated using leave-one-out (n−1) data.The sample augmentation method (OLD) is realized by multiplying thecalculated standard deviation difference by a normal random number andadding the mean value.

As shown in FIG. 35D, the sample augmented using the leave-one-out (n−1)data is used to create 1000 models to differentiate the remaining one1000 times. This is repeated n times.

(Sample Augmentation (PCA))

The schematic diagram of the analysis method using the sampleaugmentation method (PCA) is the same as those shown in FIG. 35A to FIG.35D.

In the sample augmentation (PCA), in the permutation test ofleave-one-out (n−1) data, only the miRNA that became significant is usedas a feature (feature selection). In the case of 2 class classification,for each class, the mean value is subtracted from each feature thatbecame significant, and the leave-one-out (n−1) data is used to applyPCA to calculate standard deviation from a sample projected to theprincipal component space. The standard deviation and normal randomnumber are used to apply the sample augmentation method (OLD) in theprincipal component space. The principal component coefficient is usedto carry out projection conversion from the principal component space tothe original space and add the mean value stored prior to the projectionconversion to be able to realize the sample augmentation method (PCA). Asample augmented using leave-one-out (n−1) data is used to create 1000models to differentiate the remaining one 1000 times. This is repeated ntimes.

As such, the sample augmentation method (PCA) is different from thesample augmentation method (OLD) in the point of using leave-one-out(n−1) data to apply PCA and using 1 the standard deviation sought fromthe sample projected to the principal component space and a normalrandom number to apply the sample augmentation method (OLD) in theprincipal component space.

(Parameter) The parameter in the present example is as described below.

*logistic regression (λ=0.3)*Division of a real sample (training: test=n−1:1)*Augmentation sample count

(Explanation with b as an Example)

Naïve (C0): n=500

POD28 (C2): n=500

*Trial time of “sample augmentation, model creation, evaluation”: 100times*Double cross variation (5×4 fold): augmentation sample was used.*Final test; real sample (test: 1 sample) was used.

(Result)

The result is shown in FIG. 36A to FIG. 36H.

In FIG. 36A to FIG. 36H, a model created in leave-one-out (n−1) data isused to show the result of 1000 differentiations of the remaining onesample, wherein, when the differentiation precision is extremely low(0%), it can be interpreted that the sample can be estimated as anoutlier.

As shown in FIG. 36A and FIG. 36B, the sample augmentation method (OLD)did not estimate that the sample ID3 is an outlier, but the sampleaugmentation method (PCA) estimated that this is an outlier.

As shown in FIG. 36C and FIG. 36D, the sample augmentation method (OLD)and the sample augmentation method (PCA) are different in the result ofoutlier.

As shown in FIG. 36E and FIG. 36F, the sample augmentation method (OLD)estimated that samples ID6, ID8 and ID9 are outliers, but the sampleaugmentation method (PCA) did not estimate that these are outliers.

FIG. 36G summarizes the result of FIG. 36A, FIG. 36C and FIG. 36E, andFIG. 36H is a summary of the result of FIG. 36B, FIG. 36D and FIG. 36F.

(Observation)

Even when miRNA is used for a sample, when comparing the sampleaugmentation method (OLD) and the sample augmentation method (PCA),there was a difference in the results of detection of outliers. Whenclose to 100%, the excluded 1 sample would be predictable using thesample augmentation method, but when close to 0%, the sample can beinterpreted as unpredictable. Since an outstanding correction percentageclose to 100% or 0% can be calculated when using the sample augmentationmethod (PCA), it can be considered that this can be considered as anexcellent outlier detection tool.

As such, it can be understood that sample augmentation enables machinelearning while retaining quality.

Example 4: Augmentation of Pain Analysis Result=Closed Eye SampleAugmentation

In the present example, a closed eye sample is used to carry out painanalysis. Upon doing so, sample augmentation was carried out.

(Method and Material)

(Closed Eye Sample)

A closed eye sample refers to reaction data against stimulation of whenthe eyes of a subject are closed. In this example, in the eye-closingtask of having the subject close the eyes, reaction data, which isbrainwave data herein, against some gradual thermal stimulations from“no pain (36° C.)” to “having pain (48° C.)” was obtained. “No pain (36°C.)” shows the state of when there is 36° C. of thermal stimulation and“having pain (48° C.)” shows a state of when there is 48° C. of thermalstimulation.

The experimentation trial is as described below.

(1) pre: gradual thermal stimulation (36° C. to 48° C.): referencestimulation imposed on a subject beforehand(2) main: after surgery (long-time (6 hours) measurement at bed side)

(COVAS Template)

(1) of the experimentation trial was carried out to a plurality ofhealthy people (N=150) beforehand to obtain the COVAS data of N=150. ACOVAS template was prepared beforehand by calculating the mean value ofthe COVAS data thereof. The COVAS template corresponds the gradualthermal stimulation of (1) of the experimentation trial to thesubjective evaluation of the pain of the healthy people.

FIG. 37A shows an example of the gradual thermal stimulation of (1) ofthe experimentation trial, example of COVAS template correspondingthereto, and an example of a sorted COVAS template in which COVAStemplates are sorted in ascending order from the minimum value of 0 tothe maximum value of 100.

(Preprocessing)

The sampling rate was set to 500 Hz.

The brainwave was measured by using a total of 18 ch, which are 6ch ofthe forehead (monopolar electrode arrangement) added with the 6 ch ofthe bipolar electrode arrangement and the 6 ch of CAR (Common AverageReference) electrode arrangement.

Brainwave data of 18 channels was cut out in the length of the COVAStemplate created beforehand with a trigger showing the initiation timingof thermal stimulation (pain stimulation) as the initiation point. Thiscauses the COVAS template to match with the length of the brainwave dataof 18 channels. The COVAS template can be corresponded as a label to thebrainwave data used for learning by matching the COVAS template with thelength of the brainwave data of 18 channels. In other words, thesubjective evaluation of pain would be corresponded to the brainwavedata used for learning.

The data was collected while being divided into data for model creationand data for test (actual performance) in clinical experimentation. (1)of the experimentation trial is for model creation and (2) of theexperimentation trial is for test. The time window of 16 seconds was cutout while shifting one second at a time with respect to the entirelength of the brainwave data. The time window is shifted in thedirection of the time axis to cut out a plurality of times to generate aplurality of original samples.

As a pre-processing, a dedicated noise processing method is applied tothe plurality of original samples. Brainwave data of each channel wascut out so as to secure 9 sequences while shifting an 8-second window 1second at a time with respect to a 16-second brainwave data of theoriginal sample that underwent pre-processing. 4 types of features,absolute amplitude, entropy, frequency power from 8 frequency bands (2-5Hz, 5-8 Hz, 8-14 Hz, 14-28 Hz, 28-58 Hz, 62-118 Hz, 122-178 Hz and182-238 Hz) and coherence, are extracted from the brainwave data of eachchannel. The four types of features (amplitude, frequency power,coherence and entropy) were bound and a total of 324 features wereextracted. 324×9 features in which 324 feature and 9 chronologicalsequences are a unit were obtained thereby.

With respect to the extracted feature, a sample augmentation method isapplied to each individual to increase the number of samples. Theincreased samples are used to create a model to be fitted to theindividual using LSTM (Long short-term memory).

(Definition of Standardization Parameter for Searching a Model)

COVAS templates created beforehand were sorted in ascending order fromthe minimum value of 0 to the maximum value of 100. From the sortedCOVAS templates, 19 ranges were cut out from the minimum value 0 to themaximum value 1000 in the unit of 10 while shifting 5 at a time. These19 ranges are 19 types of standardization parameters, wherein the meanvalue and standard deviation of each of these 19 types ofstandardization parameters are calculated. 19 mean values and 19standard deviations are each preserved for use upon the off-linechronological data analysis later on.

(Standardization of Feature Data by 10 Standardization Parameters)

From the sorted COVAS templates, 10 ranges were cut out from the minimumvalue of 0 to the maximum value of 100 in the in the unit of 10 whileshifting 10 at a time. Since these 10 ranges are 10 types ofstandardization parameters wherein the COVAS template and the brainwavedata are corresponded, features corresponded to the 10 types ofstandardization parameters are extracted. The extracted features werestandardized (turned into z value) using a corresponding standardizationparameter.

With respect to 10 standardized features, the following steps arerepeatedly carried out to create 10 models (LSTM regression).

1) Regression: (Sample augmentation) Upon learning, when labels areequally present, it is understood that it is easier to acquiregeneralization capability, and thus, upon sample augmentation, aparameter regulating the number of augmentation samples for each labelis defined so that the proportion of the values of the labels of the(sorted) COVAS templates corresponding to each feature would be equal.

2) Regression: (Sample augmentation) 5 samples are used as a unit andthe samples generated by a random number from the multivariate normaldistribution based on the mean value and covariance matrix thereof areincreased by the parameters defined for each label in 1). The samplesare increased by the number of repetition.

3) Regression: (Model creation: (learning)) The augmented sample isdefined as a learning sample to be learned together with a correspondinglabel to create a model by LSTM regression.

FIG. 37B shows the range of 19 types of standardization parameters cutout from the sorted COVAS templates and 10 models corresponding to 10types of standardization parameters.

(Off-Line Chronological Data Analysis)

In order to search for the best combination from the combinations of 19standardization parameters and 10 models, the 19 standardizationparameters and 10 models are used to calculate the result of 190regressions. In the off-line chronological data analysis, features werefirst extracted with respect to the entirety of the time direction oftest data. The data after feature extraction was retained in anunstandardized state (unstandardized feature). With respect to theunstandardized feature, each of the 19 standardized parameters is usedto carry out standardization (turning into z value) to calculate thestandardization feature. In other words, regarding the i-thstandardization parameter among the 19 standardization parameters(0<i≥19), when mean pi and standard deviation σ_(i) are set andunstandardized feature is set as x and the standardized featureregarding the standardization parameter i is set as x′_(i), calculationis carried out by: x′_(i)=(x−μ_(i))/σ_(i)

The pain score was predicted by administering a standardization featureto a model.

In this example, regarding 4 types of models among the 10 types ofmodels, only the diagonal component of 10×19 matrix is used to carryingout ensemble learning of the pain score (prediction value of regression)to calculate the correlation function and RMSE (Root Mean Square Error)to display the result of regression.

(Result)

FIG. 37C shows the result of the present example.

In FIG. 37C, the matrix on the left side expresses the combination ofthe used 19 standardization parameters and 10 models, wherein the rowexpresses the model and the column expresses the standardizationparameter. The colored cell shows the selected combination. The ensemblelearning (i.e., mean value) of what was selected corresponds to the painscore (black line) of the graph on the right side of FIG. 37C. The grayline of the graph on the right side of FIG. 37C shows NRS which is asubjective evaluation of the pain of a patient. The position of thetriangle formed in a dotted line shows the timing when NRS is asked tothe patient.

Among the 4 graphs, the first graph from the top shows the result ofwhen using the first to fourth models and first to seventhstandardization parameters, the second graph from the top shows theresult of when using the fourth to seventh model and the seventh tothirteenth standardization parameters, the third graph from the topshows the result of when using the seventh to tenth models andthirteenth to nineteenth standardization parameters, and the fourthgraph from the top shows the result of when using all models andstandardization parameters.

It can be understood from the result of FIG. 37C that NRS corresponds tothe pain score to some extent.

Example 5: Augmentation of Pain Analysis Result=Closed Eye SampleAugmentation

In the present example, a closed eye sample is used to carry out painanalysis. Upon doing so, sample augmentation was carried out.

(Method and Material)

(Closed Eye Sample)

A closed eye sample refers to reaction data against stimulation of whenthe eyes of a subject are closed. In this example, in the eye-closingtask of having the subject close the eyes, reaction data, which isbrainwave data herein, against some gradual thermal stimulations from“no pain (36° C.)” to “having pain (48° C.)” was obtained. “No pain (36°C.)” shows the state of when there is 36° C. of thermal stimulation and“having pain (48° C.)” shows a state of when there is 48° C. of thermalstimulation.

The experimentation trial is as described below.

An experimentation (minimum_set_heat), in which minimum data obtainmentwould be enough, which was carried out for algorithm development, wascarried out.

(1) First minimum_set_heat: gradual thermal stimulation (36° C. to 48°C.)(2) Second minimum_set_heat: gradual thermal stimulation (36° C. to 48°C.)

In the minimum_set_heat, thermal stimulation, which the thermalstimulation was increased from 36° C. to 48° C. in a step-like manner,and the decreased from 48° C. to 36° C. in a step-like manner, wasimposed.

(COVAS Template)

(1) of the experimentation trial was carried out to a plurality ofhealthy people (N=150) beforehand to obtain the COVAS data of N=150. ACOVAS template was prepared beforehand by calculating the mean value ofthe COVAS data thereof. The COVAS template corresponds the gradualthermal stimulation of (1) of the experimentation trial to thesubjective evaluation of the pain of the healthy people.

(Preprocessing)

The sampling rate was set to 1000 Hz.

The brainwave was measured by using a total of 18 ch, which are 6ch ofthe forehead (monopolar electrode arrangement) added with the 6 ch ofthe bipolar electrode arrangement and the 6 ch of CAR (Common AverageReference) electrode arrangement.

Brainwave data of 18 channels was cut out in the length of the COVAStemplate created beforehand with a trigger showing the initiation timingof thermal stimulation (pain stimulation) as the initiation point. Thiscauses the COVAS template to match with the length of the brainwave dataof 18 channels. The COVAS template can be corresponded as a label to thebrainwave data used for learning by matching the COVAS template with thelength of the brainwave data of 18 channels. In other words, thesubjective evaluation of pain would be corresponded to the brainwavedata used for learning.

The data was collected while being divided into data for model creationand data for test (actual performance). (1) of the experimentation trialis for model creation and (2) of the experimentation trial is for test.The time window of 8 seconds was cut out while shifting one second at atime with respect to the entire length of the brainwave data. The timewindow is shifted in the direction of the time axis to cut out aplurality of times to generate a plurality of original samples.

As a pre-processing, a dedicated noise processing method is applied tothe plurality of original samples. Brainwave data of each channel wascut out so as to secure 9 sequences while shifting an 8-second window 1second at a time with respect to a 16-second brainwave data of theoriginal sample that underwent pre-processing. 4 types of features,absolute amplitude, entropy, frequency power from 8 frequency bands (2-5Hz, 5-8 Hz, 8-14 Hz, 14-28 Hz, 28-58 Hz, 62-118 Hz, 122-178 Hz and182-238 Hz) and coherence, are extracted from the brainwave data of eachchannel. The four types of features (amplitude, frequency power,coherence and entropy) were bound and a total of 324 features wereextracted. 324×9 features in which 324 feature and 9 chronologicalsequences are a unit were obtained thereby.

With respect to the extracted feature, a sample augmentation method isapplied to each individual to increase the number of samples. Theincreased samples are used to create a model to be fitted to theindividual using LSTM (Long short-term memory).

(Definition of Standardization Parameter for Searching a Model)

COVAS templates created beforehand were sorted in ascending order fromthe minimum value of 0 to the maximum value of 100. From the sortedCOVAS templates, 10 ranges were cut out from the minimum value 0 to themaximum value 1000 in the unit of 10 while shifting 10 at a time. Theseranges are 10 types of standardization parameters, wherein the meanvalue and standard deviation of these 10 types of standardizationparameters are calculated. 10 mean values and 10 standard deviations areeach preserved for use upon the off-line chronological data analysislater on.

(Standardization of Feature Data by 10 Standardization Parameters)

From the sorted COVAS templates, 10 ranges were cut out from the minimumvalue of 0 to the maximum value of 100 in the in the unit of 10 whileshifting 10 at a time. Since these 10 ranges are 10 types ofstandardization parameters wherein the COVAS template and the brainwavedata are corresponded, features corresponded to the 10 types ofstandardization parameters are extracted. The extracted features werestandardized (turned into z value) using a corresponding standardizationparameter.

With respect to 10 standardized features, the following steps arerepeatedly carried out to create 10 models (LSTM regression).

1) Regression: (Sample augmentation) Upon learning, when labels areequally present, it is understood that it is easier to acquiregeneralization capability, and thus, upon sample augmentation, aparameter regulating the number of augmentation samples for each labelis defined so that the proportion of the values of the labels of the(sorted) COVAS templates corresponding to each feature would be equal.

2) Regression: (Sample augmentation) 5 samples are used as a unit andthe samples generated by a random number from the multivariate normaldistribution based on the mean value and covariance matrix thereof areincreased by the parameters defined for each label in 1). The samplesare increased by the number of repetition.

3) Regression: (Model creation: (learning)) The augmented sample isdefined as a learning sample to be learned together with a correspondinglabel to create a model by LSTM regression.

FIG. 38A shows the range of 19 types of standardization parameters cutout from the sorted COVAS templates and 10 models corresponding to 10types of standardization parameters.

(Off-Line Chronological Data Analysis)

In order to search for the best combination from the combinations of 10standardization parameters and 10 models, the 10 standardizationparameters and 10 models are used to calculate the result of 100regressions. In the off-line chronological data analysis, features werefirst extracted with respect to the entirety of the time direction oftest data. The data after feature extraction was retained in anunstandardized state (unstandardized feature). With respect to theunstandardized feature, each of the 10 standardized parameters is usedto carry out standardization (turning into z value) to calculate thestandardization feature. In other words, regarding the i-thstandardization parameter among the 10 standardization parameters(0<i≤10), when mean pi and standard deviation σ_(i) are set andunstandardized feature is set as x and the standardized featureregarding the standardization parameter i is set as x′_(i), calculationis carried out by: x′_(i)=(x−μ_(i))/σ_(i)

The pain score was predicted by administering a standardization featureto a model.

In this example, among 10×10 matrix, top several numbers of pieces (top1, top 5, top 10, all) that satisfy the standard are secured beforehandbased on the standard of threshold consisting of a correlation functionand RMSE, wherein ensemble learning of pain score (prediction value ofregression) is carried out for each condition and the correlationcoefficient and RMSE (Root Mean Square Error) are calculated to displaythe result of the regression.

(Result)

FIG. 38B shows the result of the present example.

In FIG. 38B, the matrix on the left side expresses a combination of 10standardization parameters and 10 models that have been used, whereinthe row expresses a model and the column expresses a standardizationparameter. Coloring is carried out based on whether or not thecorrelation coefficient and RMSE satisfy the threshold. The slantedlines express a combination in which RMSE is smaller than the threshold,the stipple expresses a combination in which the correlation coefficientis higher than the threshold, and the light color expresses acombination in which RMSE is smaller than the threshold and thecorrelation coefficient is higher than the threshold. The higher thecorrelation coefficient showing how well the fitting is, the betterstandard would be set, and the lower the RMSE showing an error, thebetter standard would be set. Among the light color combinations, thoseselecting the top 1, top 5, top 10 and all that satisfy the standard ofRMSE being smaller than the threshold and the correlation coefficientbeing higher than the threshold are shown with the dark color, whereineach result is shown in the first graph, second, graph, third graph andfourth graph from the top, respectively.

The graph on the right side of FIG. 38B corresponds to the result ofwhen using the combination on the left side. The ensemble learning ofthe combinations selected with the dark color, i.e., mean value,expresses the pain score (back line: prediction value), and the grayline shows the template of COVAS (actual measurement value) which is thesubjective evaluation of pain of a patient.

It can be understood from the result of FIG. 38B that the COVAS template(actual measurement value) corresponds to the pain score (predictionvalue) to some extent.

(Note)

As disclosed above, the present disclosure has been exemplified by theuse of its preferred embodiments. However, it is understood that thescope of the present disclosure should be interpreted based solely onthe Claims. It is also understood that any patent, patent application,and references cited herein should be incorporated herein by referencein the same manner as the contents are specifically described herein.The present application claims priority to Japanese Patent ApplicationNo. 2019-85782 filed on Apr. 26, 2020 with the Japan Patent Office. Theentire content thereof is incorporated herein by reference.

INDUSTRIAL APPLICABILITY

The present disclosure is useful upon providing a system and the likefor augmenting supervisory data while maintaining the relationship amonga plurality of supervisory data used for machine learning.

REFERENCE SIGNS LIST

-   100: system-   110: obtaining means-   120: processor-   130: memory-   140: output means-   1000: object-   1100: system comprising a pain level differentiation/estimation    apparatus-   1110: pain level differentiation/estimation apparatus-   1111: measurement unit-   1112: feature extraction unit-   1113: pain index generation unit-   1114: standard determination unit-   1115: pain level monitoring unit-   1120: electroencephalograph-   101000: feature contracting unit-   102000: feature extraction unit-   103000: pain differentiation/estimation model generation unit-   104000: pain differentiation/estimation unit-   105000: reference stimulation application unit-   105200: brainwave data measurement unit-   106000: object-   107000: pain level visualization unit-   108000: apparatus-   110000: brainwave data measurement unit-   120000: data transceiver unit-   125000: data transceiver unit-   130000: pain level visualization unit-   135000: pain level visualization unit-   140000: brainwave feature extraction unit-   145000: brainwave feature extraction unit-   150000: pain level differentiation estimation unit-   160000: pain differentiation model generation unit-   170000: data storage unit-   180000: brainwave database-   301000: feature contracting unit-   302000: feature extraction unit-   303000: pain differentiation/estimation model generation unit-   304000: pain differentiation/estimation unit-   305000: reference stimulation application unit-   305200: brainwave data measurement unit-   306000: object-   307000: pain level visualization unit-   307500: data storage unit-   308000: apparatus

1. A system for augmenting supervisory data used for machine learning,comprising: an obtaining means obtaining a plurality of supervisorydata; a first processing means deriving a covariance matrix from theplurality of supervisory data; a second processing means decomposing thecovariance matrix; and a third processing means applying a random numberto the decomposed matrix.
 2. The system of claim 1, wherein thesupervisory data is data obtained from an organism.
 3. The system ofclaim 2, wherein the supervisory data is brainwave data, MRI image data,or gene expression data.
 4. The system of claim 3, wherein thesupervisory data is brainwave data or MRI data of when pain is appliedto the organism.
 5. The system of claim 1, wherein: the secondprocessing means is configured to decompose the covariance matrix intoQ*Q′, wherein matrix Q′ is a transposed matrix of matrix Q; and thethird processing means is configured to apply a random number to thematrix Q or the matrix Q′.
 6. The system of claim 5, wherein: the secondprocessing means is configured to decompose the covariance matrix intoQ*Q′ by carrying out one of Cholesky decomposition, LU decomposition andQR decomposition to the covariance matrix, wherein the matrix Q ormatrix Q′ is an upper triangular matrix; and the third processing meansis configured to apply a random number to the upper triangular matrix.7. The system of claim 1, wherein: the first processing means is furtherconfigured to calculate a mean value vector of the plurality ofsupervisory data; and the third processing means is further configuredto add a mean value vector to the decomposed matrix to which the randomnumber has been applied.
 8. The system of claim 1, wherein: the firstprocessing means is configured to calculate a mean value of theplurality of supervisory data, subtract a mean value from the pluralityof supervisory data, and derive a covariance matrix from a plurality ofsupervisory data in which the mean value has been subtracted; the secondprocessing means is configured to decompose the covariance matrix intoV*L*V′, wherein matrix L is a diagonal matrix consisting of aneigenvalue, matrix V is a matrix having a corresponding righteigenvector in a column, and matrix V′ is a transposed matrix of matrixV, wherein when sqrt( ) is set as a function employing a square root,matrix M=sqrt(L) is expressed, and the third processing means isconfigured to apply a random number to the matrix M, wherein the systemfurther comprises a fourth processing means carrying out projectionconversion of the matrix M to which the random number has been applied,wherein the fourth processing means adds the mean value to matrix M thatunderwent projection conversion.
 9. The system of claim 1, furthercomprising a dividing means dividing the plurality of supervisory datainto a plurality of subsets, wherein the first processing means, thesecond processing means and the third processing means carry out eachprocessing to each of the plurality of subsets.
 10. A pain estimationsystem estimating pain that an object being measured has, comprising:the system for augmenting supervisory data used for machine learning ofclaim 1; and a system for learning a plurality of supervisory dataaugmented by the system for augmenting the supervisory data andgenerating a pain estimation model.
 11. A method for augmentingsupervisory data used for machine learning, comprising: obtaining aplurality of supervisory data; deriving a covariance matrix from theplurality of supervisory data; decomposing the covariance matrix; andapplying a random number to the decomposed matrix.
 12. A computerreadable non-transient recording medium on which a program foraugmenting supervisory data used for machine learning is recorded,wherein the program is performed in a computer system comprising aprocessor, wherein the program causes the processor unit to perform aprocessing comprising: obtaining a plurality of supervisory data;deriving a covariance matrix from the plurality of supervisory data;decomposing the covariance matrix; and applying a random number to thedecomposed matrix.
 13. A method of generating a pain classifier forclassifying pain that an object being estimated has based on a brainwaveof the object being estimated, comprising: a) the step of stimulatingthe object being estimated with a plurality of levels of stimulationintensities; b) the step of obtaining brainwave data or analysis datathereof of the object being estimated corresponding to the stimulationintensity; c) the step of augmenting brainwave data or analysis datathereof of the object being estimated, comprising: i) deriving acovariance matrix from brainwave data or analysis data thereof of theobject being estimated; ii) decomposing the covariance matrix; and iii)applying a random number to the decomposed matrix; d) the step ofplotting the stimulation intensity or a subjective pain sensation levelcorresponding to the stimulation intensity and the augmented brainwavedata or analysis data thereof to fit to a pain function to obtain a painfunction specific to the object being estimated; and e) the step of,when regression coefficient of the fitting to the specific pain functionis equal to or more than what is predetermined, identifying a painclassifier for dividing a pain level into at least two or more based onthe specific pain function.
 14. An apparatus generating a painclassifier for classifying pain that an object being estimated has basedon a brainwave of the object being estimated, comprising: A) astimulation unit stimulating the object being estimated with a pluralityof levels of stimulation intensities; B) a brainwave data obtaining unitobtaining brainwave data or analysis data thereof the object beingestimated corresponding to the stimulation intensity; C) an augmentationunit augmenting brainwave data or analysis data thereof of the objectbeing estimated, wherein the augmentation unit is configured to: i)derive a covariance matrix from brainwave data or analysis data thereofof the object being estimated; ii) decompose the covariance matrix; andiii) apply a random number to the decomposed matrix; and D) a painclassifier generation unit plotting the stimulation intensity or asubjective pain sensation level corresponding to the stimulationintensity and the augmented brainwave data or analysis data thereof tofit to a pain function to obtain a pain function specific to the objectbeing estimated and identifying a pain classifier for dividing a painlevel into at least two or more based on the specific pain function. 15.A method of generating a model for differentiating pain of an object,comprising: a) the step of obtaining brainwave data or analysis datathereof from the object; b) the step of contracting features based onthe brainwave data or analysis data thereof with respect to the painafter determining a feature coefficient associated with the pain; c)augmenting the features that have been weighted after the contracting orcombination thereof, comprising: i) deriving a covariance matrix fromthe features that have been weighted after the contracting orcombination thereof, ii) decomposing the covariance matrix; and iii)applying a random number to the decomposed matrix; d) the step ofcreating a differentiation analysis model by machine learning andexamination based on the augmented features or combination thereof, ande) the step of determining a differentiation analysis model achieving apredetermined precision.
 16. A system generating a model fordifferentiating pain of an object, the system comprising: A) a brainwavedata obtaining unit obtaining brainwave data or analysis data thereoffrom the object; B) a feature contracting unit contracting featuresbased on the brainwave data or analysis data thereof with respect to thepain after determining a feature coefficient associated with the pain;C) an augmentation unit augmenting the features that have been weightedafter the contracting or combination thereof, wherein the augmentationunit is configured to: i) derive a covariance matrix from the featuresthat have been weighted after the contracting or combination thereof,ii) decompose the covariance matrix; iii) apply a random number to thedecomposed matrix; and D) a pain differentiation/estimation modelgeneration unit creating a differentiation analysis model by machinelearning and examination based on the augmented features that have beenweighted after the contracting or combination thereof.
 17. A method ofanalyzing an organism as an object, comprising: a) the step ofstimulating the organism with a plurality of types of stimulations; b)the step of obtaining reaction data of the organism corresponding to thestimulation type; c) augmenting reaction data of the organism,comprising: i) deriving a covariance matrix from reaction data of theorganism; ii) decomposition the covariance matrix; and iii) applying arandom number to the decomposed matrix; and d) the step of plotting thestimulation type and the augmented reaction data for analysis.
 18. Themethod of claim 17, wherein stimulation of the organism is stimulationto gene or candidate of an agent, and the reaction data comprises geneexpression data or reaction of an organism.
 19. (canceled) 20.(canceled)